Dave Keenan & Douglas Blumeyer's guide to RTT: alternative complexities
In this article we will investigate one of the most advanced tuning concepts that have been developed for regular temperaments: alternative complexities (besides logproduct complexity)
This is article 8 of 9 in Dave Keenan & Douglas Blumeyer's guide to RTT, or "D&D's guide" for short. It assumes that you have read all of these prior articles in the series:
 3. Tuning fundamentals: learn about minimizing damage to targetintervals
 5. Units analysis: to look at temperament and tuning in a new way, think about the units of the values in frequently used matrices
 6. Tuning computation: for methods and derivations; learn how to compute tunings, and why these methods work
 7. Allinterval tuning schemes: the variety of tuning most commonly named and written about on the Xenharmonic wiki
In particular, most of these advanced tuning concepts have been developed in the context of allinterval tuning schemes, so a firm command of the ideas in that article will be very helpful for getting the most out of this article.
Introduction
So far, we've used three different functions for determining interval complexity:
 In an early article of this series, we introduced the most obvious possible function for determining the complexity of a rational number [math]\frac{n}{d}[/math] such as those that represent JI intervals: product complexity, the product of the ratio's numerator and denominator.
 However, since that time, we've been doing most of our work using a variant on this function, not product complexity itself; this variant is the logproduct complexity, which is the base2 logarithm of product complexity.
 It wasn't until late in our allinterval tuning schemes article that we introduced our third complexity function, which arises in the explanation of a popular tuning scheme that we call minimaxES; this uses the Euclideanized version of the logproduct complexity.
But these three functions are not the only ones that have been discussed in the context of temperament tuning theory. In this article we will be taking a brief look at several other functions which some theorists have investigated for possible use in the tuning of regular temperaments:
 sumofprimefactorswithrepetition (sopfr) complexity
 countofprimefactorswithrepetition (copfr) complexity
 logintegerlimitsquared (lils) complexity
 logoddlimitsquared (lols) complexity
We'll also consider the Euclideanized versions of each of these four functions.
This is not meant to be an exhaustive list of complexity functions that may be argued to have relevance for tuning theory. It seems like every few months someone suggests a new one. We prepared this article as a survey of these relatively wellestablished use cases in order to demystify what's out there already and to demonstrate some principles of this nook of the theory, in case this is an area our readers would like to explore further.
Not just for allinterval tuning schemes
These alternative complexity functions were all introduced to regular temperament theory through the use of allinterval tuning schemes, the kind that use a series of clever mathematical tricks in order to avoid the choice of a specific, finite set of intervals to be their tuning damage minimization targets.
Historically speaking, ordinary tuning schemes —those which do tune a specific finite targetinterval set — have not involved complexity functions at all, not even logproduct complexity. For the most part, anyway, users of ordinary tuning schemes have instead leaned on the careful choice of their targetintervals, achieving fine control of their tuning in this way, and it has been sufficient for them to define damage as the absolute error to their targetintervals. In other words, they have not felt the need to weight their chosen targetintervals' errors relative to each other according to the intervals' complexities.
The argument could be made, then, that this lack of control over the tuning via the choice of specific targetintervals is what caused allinterval tuning schemes to become such a breeding ground for interest in alternative complexity functions. With allinterval tuning schemes, this was the only other way they could achieve that sort of fine control.
And so, while allinterval tuning schemes are where these innovations in complexity functions occurred historically, we note that there's nothing restricting their use to allinterval tuning schemes. There's no reason why ordinary tuning schemes cannot take advantage of both opportunities for fine control that are available to them: targetinterval set choice, and alternative complexity functions. In fact, we will later argue that some of these alternative complexity functions may actually be more appropriate to use for ordinary tuning schemes than they are for allinterval tuning schemes.
Naming
The use of alternative functions for determining complexity is what accounts for the profusion of eponymous tuning schemes you may have come across: Benedetti, Weil, Kees, and Frobenius. This is because some theorists have preferred to refer to these functions by a person's name — whether this is the discoverer of the generic math function, the first person to apply it to tuning, or just a person somehow associated with the function — perhaps because eponyms are distinctive and memorable. But if you're like us, the connection between these historical personages and these functions is not obvious, and so we can never seem to remember which one of these tuning schemes is which! We've had to rely on awkward mnemonics to keep the mapping from eponym to function straight. So if you're struggling with this as well, then we hope you'll appreciate and adopt our tuning scheme naming system which drops the eponyms in favor of the actual names of the math functions used, e.g. the minimaxsopfrS tuning scheme minimaxes the sopfrsimplicityweight damage to all intervals, where "sopfr" is a standard math function that you can look up, which stands for "sum of prime factors with repetitions" (isn't that better than "Benedetti Optimal"?). Because the math function names are descriptive, we encode meaning rather than encrypt it. And by naming them systematically, we isolate their differences through the structural patterns shared among each name, so we can contrast them at a glance.
This approach is also good because it can accommodate any complexity one might dream up; as long as you can name the complexity function, you can use its name in the name of the corresponding tuning scheme.
This is an extension of the tuning scheme naming system initially laid out at the end of the tuning fundamentals article Dave Keenan & Douglas Blumeyer's guide to RTT: tuning fundamentals#Systematic tuning scheme names, and so it also allows for easy comparison with those simpler, ordinary tuning schemes, e.g. the (allinterval) minimaxsopfrS scheme is very closely related to the TILT minimaxsopfrS scheme, which is the same except instead of using prime proxy targets in order to target all intervals, it specifically targets only the TILT. So basically the name breaks down into [optimization][complexity][damage weight slope]. The [complexity] part can be broken down further into [norm power if q≠1][q=1 complexity]; for example, in minimaxElilsS, we have minimax is our optimization, Elils (Euclideanized logintegerlimitsquared) is our complexity function, and S (simplicityweight) is our damage weight slope. And our complexity can be further broken down into 'E' which is its norm power other than taxicab (q=1) ("Euclideanized" means q=2) and "lils" is the logintegerlimitsquared, which is what the complexity function would be, when using the taxicab norm.
Comparing overall complexity qualities
In this first section, we'll be comparing how these functions rank intervals differently. This information is important because if you don't feel that a function does an effective job at determining the relative complexity of musical intervals, then you probably shouldn't use it to tune temperaments of them! Well, you might choose to anyway, if you perceived a worthwhile tradeoff, such as computational expediency. We'll save discussion of that until later.
Complexity contours
Here's a diagram comparing 10 of our 12 functions (not including "product" or its Euclideanized version; we'll explain why later).
With this diagram, you can look at how each function sorts intervals, and decide for yourself whether you think that's a reasonable order, i.e. does it make musical sense, as a way you might weight errors, whether complexityweighted or simplicityweighted. (These happen to be shown simplicityweighted.) And here's a diagram where they're all sorted according to product complexity. This one, then, comes at the problem from the assumption that product complexity is the "correct" way to sort them, so we can see in the textures of these curves to what extent they deviate from it. This is an assumption we mostly hold (deets on dispute later). You can see how the Euclideanized versions are all worse (more jagged) than their corresponding original version.
Any complexity function has a corresponding simplicity function which simply returns for any given interval the reciprocal of what the complexity function would have returned. Therefore, it is unnecessary to separately provide diagrams like these for simplicity functions. We can find the simplicity contours by turning these insideout; the ranking won't change other than the order will exactly reverse, with big bars becoming small, and small bars becoming big. We simply reciprocate each individual value.
Explaining the impact of Euclideanization
To review, Euclideanization is when we take a function which can be expressed in the form of a [math]1[/math]norm (or equivalent summation form) and — preserving its norm prescaler, if any — change its norm power from [math]1[/math] to [math]2[/math].
The 2norm is how we calculate distance in the physical reality you and I walk around within everyday: 3dimensional Euclidean space. So it may seem to be a natural choice for measuring distances between notes arranged on a JI lattice such as we might build out of rods and hubs like one might find in a chemistry modeling kit. (Perhaps some readers have actually done this as part of their learning process, or for experimenting with new scales.)
However, it is unfortunately not the case that calculating distances in this sort of space like this gives a realistic notion of harmonic distance.
Let's imagine, for example, a threedimensional JI lattice with prime 2 on one axis, prime 3 on another axis, and prime 5 on the remaining axis; so we choose a 3D example that's as similar to our physical space as can be. Now let's imagine the vectors for [math]\frac98[/math] and [math]\frac{10}{9}[/math]: [3 2 0⟩ and [1 2 1⟩, respectively, each one drawn out from the origin at unison [math]\frac11[/math] [0 0 0⟩ diagonally through the lattice. The norm for [math]\frac98[/math] is [math]\sqrt{{3}^2 + 2^2 + 0^2} = \sqrt{13} ≈ 3.605[/math], while the norm for [math]\frac{10}{9}[/math] is [math]\sqrt{1^2 + {2}^2 + 1^2} = \sqrt{6} ≈ 2.449[/math]. This suggests that [math]\frac98[/math] is a more complex interval than [math]\frac{10}{9}[/math], a suggestion that most musicians would disagree with, being that it's higher (prime) limit, and contains larger numbers.
To gain some intuition for why the [math]2[/math]norm gets this comparison wrong, begin by thinking about the space these vectors are traveling through diagonally. What is this space? Imagine stopping at some point along it, floating between nodes of the lattice — what would you say is the pitch at that point? Don't worry about these two questions too much; you probably won't be able to come up with good answers to them. But that's just the point. They don't really make sense here.
We can say that JI lattices like this are convenient tricks for helping us to visualize, understand, and analyze JI scales and chords. But there's no meaning to diagonal travel through this space. We can add a factor of 5 and remove a couple factors of 2 from [math]\frac98[/math] to find [math]\frac{10}{9}[/math], but really those are three separate, discrete actions. When we travel from [math]\frac98[/math] to [math]\frac{10}{9}[/math], we can only really think of doing it along the lattice edges, not between and through them. So while there's nothing stopping us from thinking about the space between nodes as if it were like the physical space we live in, and measuring it accordingly, in an important sense none of that space really exists, and the only distances in this harmonic world that are "real" are the distances straight along the edges of the lattice connecting each node to its neighbor nodes. In other words, diagonal travel between JI nodes is a lie. But we choose to accept this lie whenever we use a Euclideanized complexity.
Astute readers may have realized that even if we compare these same two intervals by a [math]1[/math]norm, i.e. where we measure their distance from the unison at the origin by a series of separate straight segments along the edges between pitches, we still find that the norm for [math]\frac98[/math] is [math]\sqrt[1]{{3}^1 + 2^1 + 0^1} = 5[/math], while the norm for [math]\frac{10}{9}[/math] is [math]\sqrt[1]{1^1 + {2}^1 + 1^1} = 4[/math], that is, we still find that [math]\frac98[/math] is ranked more complex than [math]\frac{10}{9}[/math]. The remaining discrepancy is that we haven't scaled the lattice so that edge lengths are proportional to the sizes of the primes in pitch, which is to say, scaled each axis according to the logarithm of the prime that axis is meant to represent. If every occurrence of a prime 2 is the standard complexity point of 1, but prime 3 counts for [math]\log_2{3} \approx 1.585[/math] points, and prime 5 counts for [math]\log_2{5} \approx 2.232[/math] points, then we see [math]\frac98[/math] come out to [math]{3}×1 + 2×1.585 + 0×2.232 = 6.170[/math] and [math]\frac{10}{9}[/math] come out to [math]1·1 + {2}×1.585 + 1×2.232 = 6.402[/math], which finally gives us a reasonable comparison between these two intervals. This logarithmicallyscaled [math]1[/math]norm may be recognized as logproduct complexity. As for unscaled [math]1[/math]norm — that's actually the count of prime factors with repetition, a different one of the functions we'll be looking at in more detail soon. You can find [math]\frac98[/math] and [math]\frac{10}{9}[/math] in the previous section's diagrams and see how these are all ranked differently by the different complexities to confirm the points made right here.
Why do we (or some theorists anyway) accept the lie of Euclideanized harmonic space sometimes? The answer is: computational expediency. It's not easier to compute individual [math]2[/math]norms than it is to compute individual [math]1[/math]norms, of course. However, it turns out that it is easier to compute the entire optimized tuning of a temperament when you pretend that Euclidean distance is a reasonable way to measure the complexity of an interval, than it is when you insist on measuring it using the truer alongtheedges distance. We can therefore think of Euclideanized distance as a decent approximation of the true harmonic distances, which some theorists decide is acceptable.
Our opinion? There are very few situations where the difference in computation speed for a tuning optimized with a [math]1[/math]norm and a [math]2[/math]norm will make a difference. Perhaps an automated script that runs upon loading a web page, where web page load times are famously important to get as lighting fast as possible. But in general, if you know how to compute the answer with a [math]1[/math]norm, the computer is going to calculate the answer just about as fast as it would with a [math]2[/math]norm approximation, so just use the [math]1[/math]norm. The listeners of your music for eternities to come will thank you for giving your computer a few extra seconds to get the tunings as nice as you knew how to ask it for them.
Why we don't "maxize"
You may wonder why we have three norm powers of special interest — [math]1[/math], [math]2[/math], and [math]∞[/math] — yet the only norms we're looking at are prescaled (or not prescaled) [math]1[/math]norms (original) or [math]2[/math]norms (Euclideanized). Why do we not consider any [math]∞[/math]norms?
The short answer is: because no theorist has yet demonstrated significant interest in them. And the reason for that is: because they're even less harmonically realistic than Euclideanized norms, but without a computational expediency boost to counteract this. Here's a diagram that shows what happens to interval rankings with a [math]∞[/math] norm, showing how they're even more scrambled than before:
And we can also think of this in terms of what measuring this distance would be like. Please review the way of conceptualizing maxization given here: Dave Keenan & Douglas Blumeyer's guide to RTT: allinterval tuning schemes#Maxization, and then realize that if the shortcoming of measuring harmonic distance with Euclideanized measurement is how it takes advantage of the nottrulyexistent diagonal space between lattice nodes, then maxized measurement is the absolute extreme of that shortcoming, or should we say shortcutting, in that it allows you to go as diagonally as possible at all times for no cost.
Why we don't use any other norm power
Too complicated? Who cares? But seriously, no theorist has yet demonstrated interest in any norm power for complexities other than [math]1[/math] or [math]2[/math], whether [math]∞[/math] or otherwise.
Prescaling vs pretransforming
Some of the alternative complexities discussed in this article cannot be said to merely prescale the vectors they take the norm of. For example, the integerlimit type complexities have a shearing effect. So, we may tend to prefer the more general term pretransform in this article, and accordingly the complexity pretransformer [math]X[/math] and inverse pretransformer [math]X^{1}[/math].
The complexities
Here's a monster table, listing all the complexities we'll look at in this article.
[math] % \slant{} command approximates italics to allow slanted bold characters, including digits, in MathJax. \def\slant#1{\style{display:inlineblock;margin:.05em;transform:skew(14deg)translateX(.03em)}{#1}} % Latex equivalents of the wiki templates llzigzag and rrzigzag for double zigzag brackets. % Annoyingly, we need slightly different Latex versions for the different Latex sizes. \def\smallLLzigzag{\hspace{1.4mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.05em);fontfamily:sansserif}{ꗨ\hspace{2.6mu}ꗨ}\hspace{1.4mu}} \def\smallRRzigzag{\hspace{1.4mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.05em);fontfamily:sansserif}{ꗨ\hspace{2.6mu}ꗨ}\hspace{1.4mu}} \def\llzigzag{\hspace{1.6mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.07em);fontfamily:sansserif}{ꗨ\hspace{3mu}ꗨ}\hspace{1.6mu}} \def\rrzigzag{\hspace{1.6mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.07em);fontfamily:sansserif}{ꗨ\hspace{3mu}ꗨ}\hspace{1.6mu}} \def\largeLLzigzag{\hspace{1.8mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.09em);fontfamily:sansserif}{ꗨ\hspace{3.5mu}ꗨ}\hspace{1.8mu}} \def\largeRRzigzag{\hspace{1.8mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.09em);fontfamily:sansserif}{ꗨ\hspace{3.5mu}ꗨ}\hspace{1.8mu}} \def\LargeLLzigzag{\hspace{2.5mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.1em);fontfamily:sansserif}{ꗨ\hspace{4.5mu}ꗨ}\hspace{2.5mu}} \def\LargeRRzigzag{\hspace{2.5mu}\style{display:inlineblock;transform:scale(.62,1.24)translateY(.1em);fontfamily:sansserif}{ꗨ\hspace{4.5mu}ꗨ}\hspace{2.5mu}} [/math]
for ordinary tuning schemes and allinterval tuning schemes  for allinterval tuning schemes only  

interval complexity norm or function  interval complexity function
in quotientbased [math]\small \frac{n}{d}[/math] form 
interval complexity norm
in vectorbased [math]\small \textbf{i}[/math] form 
retuning magnitude norm (to be minimized) dual to the interval complexity norm only has (co)vectorbased [math]𝒓[/math] form 
allinterval tuning scheme  
name
(bold = systematic) 
formula  example: [math]\small \frac{10}{9}[/math]  formula  example: [1 2 1⟩  formula  example: ⟨1.617 2.959 3.202]  name
(bold = systematic) 
[math]\small \begin{align} &\text{copfrC}(\dfrac{n}{d}) \\ &= \text{copfr}(nd) \\ \small &= \text{copfr}(n) + \text{copfr}(d) \end{align}[/math]  [math]\small = \text{copfr}(10·9) \\ \small = \text{copfr}(2·5·3·3) \\ \small = 2^0 + 5^0 + 3^0 + 3^0 \\ \small = 4[/math]  [math]\small \begin{align} &\text{copfrC}(\textbf{i}) \\ &= ‖\textbf{i}‖_1 \\ \small &= \sqrt[1]{\strut \sum\limits_{n=1}^d \mathrm{i}_n^1} \\ \small &= \sqrt[1]{\strut \mathrm{i}_1^1 + \mathrm{i}_2^1 + \ldots + \mathrm{i}_d^1} \\ \small &= \mathrm{i}_1 + \mathrm{i}_2 + \ldots + \mathrm{i}_d \end{align} [/math]  [math]\small = 1 + {2} + 1 \\ \small = 4[/math]  [math]\small \begin{align} &\text{copfrC}^{*}(𝒓) \\ &= ‖𝒓‖_∞ \\ \small &= \sqrt[∞]{\strut \sum\limits_{n=1}^d r_n^∞} \\ \small &= \sqrt[∞]{\strut r_1^∞ + r_2^∞ + \ldots + r_d^∞} \\ \small &= \max(r_1, r_2, \ldots, r_d) \end{align}[/math]  [math]\small = \max(1.617, {2.959}, 3.202) \\ \small = 3.202[/math]  
[math]\small \begin{align} &\text{EcopfrC}(\textbf{i}) \\ &= ‖\textbf{i}‖_2 \\ \small &= \sqrt[2]{\strut \sum\limits_{n=1}^d \mathrm{i}_n^2} \\ \small &= \sqrt[2]{\strut \mathrm{i}_1^2 + \mathrm{i}_2^2 + \ldots + \mathrm{i}_d^2} \\ \small &= \sqrt{\strut \mathrm{i}_1^2 + \mathrm{i}_2^2 + \ldots + \mathrm{i}_d^2} \end{align}[/math]  [math]\small = \sqrt{\strut 1^2 + 2^2 + 1^2} \\ \small = \sqrt{\strut 1 + 4 + 1} \\ \small = \sqrt6 \\ \small \approx 2.449[/math]  [math]\small \begin{align} &\text{EcopfrC}^{*}(𝒓) \\ &= \text{EcopfrC}(𝒓) \\ \small &= ‖𝒓‖_2 \\ \small &= \sqrt[2]{\strut \sum\limits_{n=1}^d r_n^2} \\ \small &= \sqrt{\strut r_1^2 + r_2^2 + \ldots + r_2^2} \end{align}[/math]  [math]\small = \sqrt{\strut 1.617^2 + 2.959^2 + 3.202^2} \\ \small \approx \sqrt{\strut 2.615 + 8.756 + 10.253} \\ \small = \sqrt{\strut 21.624} \\ \small \approx 4.650[/math]  
[math]\small \begin{align} &\text{lpC}(\dfrac{n}{d}) \\ &= \log_2(nd) \\ \small &= \log_2(n) + \log_2(d) \end{align}[/math]  [math]\small = \log_2(10·9) \\ \small = \log_2(90) \\ \small \approx 6.492[/math]  [math]\small \begin{align} &\text{lpC}(\textbf{i}) \\ &= ‖L\textbf{i}‖_1 \\ \small &= \sqrt[1]{\strut \sum\limits_{n=1}^d \log_2{\!p_n} · \mathrm{i}_n^1} \\ \small &= \sqrt[1]{\strut \log_2{\!p_1} · \mathrm{i}_1^1 + \log_2{\!p_2} · \mathrm{i}_2^1 + \ldots + \log_2{\!p_d} · \mathrm{i}_d^1} \\ \small &= \log_2{\!p_1} · \mathrm{i}_1 + \log_2{\!p_2} · \mathrm{i}_2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d \end{align}[/math]  [math]\small = \log_2{\!2} · 1 + \log_2{\!3} · {2} + \log_2{\!5} · 1 \\ \small \approx 1 + 3.170 + 2.322 \\ \small = 6.492[/math]  [math]\small \begin{align} &\text{lpC}^{*}(𝒓) \\ &= ‖𝒓L^{1}‖_∞ \\ \small &= \sqrt[∞]{\strut \sum\limits_{n=1}^d \frac{r_n}{\log_2{\!p_n}}^∞} \\ \small &= \sqrt[∞]{\strut \frac{r_1}{\log_2{\!p_1}}^∞ + \frac{r_2}{\log_2{\!p_2}}^∞ + \ldots + \frac{r_d}{\log_2{\!p_d}}^∞} \\ \small &= \max(\dfrac{r_1}{\log_2{\!p_1}}, \dfrac{r_2}{\log_2{\!p_2}}, \ldots, \dfrac{r_d}{\log_2{\!p_d}}) \end{align}[/math]  [math]\small = \max(\dfrac{1.617}{\log_2{\!2}}, \dfrac{{2.959}}{\log_2{\!3}}, \dfrac{3.202}{\log_2{\!5}}) \\ \small = \max(1.617, 1.867, 1.379) \\ \small = 1.867[/math]  
[math]\small \begin{align} &\text{ElpC}(\textbf{i}) \\ &= ‖L\textbf{i}‖_2 \\ \small &= \sqrt[2]{\strut \sum\limits_{n=1}^d \log_2{\!p_n} · \mathrm{i}_n^2} \\ \small &= \sqrt[2]{\strut \log_2{\!p_1} · \mathrm{i}_1^2 + \log_2{\!p_2} · \mathrm{i}_2^2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d^2} \\ \small &= \sqrt{\strut (\log_2{\!p_1} · \mathrm{i}_1)^2 + (\log_2{\!p_2} · \mathrm{i}_2)^2 + \ldots + (\log_2{\!p_d} · \mathrm{i}_d)^2} \end{align}[/math]  [math]\small = \sqrt{\strut (\log_2{\!2} · 1)^2 + (\log_2{\!3} · {2})^2 + (\log_2{\!5} · 1)^2} \\ \small \approx \sqrt{\strut 1 + 10.048 + 5.391} \\ \small = \sqrt{\strut 16.439} \\ \small \approx 4.055[/math]  [math]\small \begin{align} &\text{ElpC}^{*}(𝒓) \\ &= ‖𝒓L^{1}‖_2 \\ \small &= \sqrt[2]{\strut \sum\limits_{n=1}^d \frac{r_n}{\log_2{\!p_n}}^2} \\ \small &= \sqrt[2]{\strut \frac{r_1}{\log_2{\!p_1}}^2 + \frac{r_2}{\log_2{\!p_2}}^2 + \ldots + \frac{r_d}{\log_2{\!p_d}}^2} \\ \small &= \sqrt{\strut (\frac{r_1}{\log_2{\!p_1}})^2 + (\frac{r_2}{\log_2{\!p_2}})^2 + \ldots + (\frac{r_d}{\log_2{\!p_d}})^2} \end{align}[/math]  [math]\small = \sqrt{\strut (\frac{1.617}{\log_2{\!2}})^2 + (\frac{{2.959}}{\log_2{\!3}})^2 + (\frac{3.202}{\log_2{\!5}})^2} \\ \small \approx \sqrt{\strut 2.615 + 3.485 + 1.902} \\ \small = \sqrt{\strut 8.002} \\ \small \approx 2.829[/math]  
[math]\small \text{lilsC}(\dfrac{n}{d}) \\ = \log_2(\max(n, d)^2)[/math]  [math]\small = \log_2(\max(10, 9)^2) \\ \small = \log_2(10·10) \\ \small = \log_2(100) \\ \small \approx 6.644[/math]  
[math]\small \text{lilsC}(\dfrac{n}{d}) \\ = \log_2{\!nd} + \log_2{\!\frac{n}{d}}[/math]  [math]\small = \log_2(10·9) + \log_2(\frac{10}{9}) \\ \small \approx 6.492 + 0.152 \\ \small = 6.644[/math]  [math]\small \begin{align} &\text{lilsC}(\textbf{i}) \\ &= ‖ ZL\textbf{i} ‖_1 \\ \small &= ‖ \; [ \; L\textbf{i} \;  \; \smallLLzigzag L\textbf{i} \smallRRzigzag_1 \; ] \; ‖_1 \\ \small &= \sum\limits_{n=1}^d\log_2{\!p_n} · \mathrm{i}_n + {\large}\sum\limits_{n=1}^d(\log_2{\!p_n} · \mathrm{i}_n){\large} \\ \small &= \log_2{\!p_1} · \mathrm{i}_1 + \log_2{\!p_2} · \mathrm{i}_2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d \; + \\ \small & \quad\quad\quad {\large}(\log_2{\!p_1} · \mathrm{i}_1) + (\log_2{\!p_2} · \mathrm{i}_2) + \ldots + (\log_2{\!p_d} · \mathrm{i}_d){\large} \end{align}[/math]  [math]\small \begin{align} &= \log_2{\!2} · 1 + \log_2{\!3} · {2} + \log_2{\!5} · 1 \; + \\ \small & \quad\quad\quad {\large}(\log_2{\!2} · 1) + (\log_2{\!3} · {2}) + (\log_2{\!5} · 1){\large} \\[5pt] \small &= 1 + 3.170 + 2.322 + {\large}1 + {3.170} + 2.322{\large} \\ \small &= 1 + 3.170 + 2.322 + 0.152 \\ \small &\approx 6.644 \end{align}[/math]  [math]\small \begin{align} &\text{lilsC}^{*}(\textbf{𝒓}) \\ &= \max( \frac{r_1}{\log_2{\!p_1}}, \frac{r_2}{\log_2{\!p_2}}, \ldots, \frac{r_d}{\log_2{\!p_d}}, 0) \;  \\ \small & \quad\quad\quad \min(\frac{r_1}{\log_2{\!p_1}}, \frac{r_2}{\log_2{\!p_2}}, \ldots, \frac{r_d}{\log_2{\!p_d}}, 0) \end{align}[/math]  [math]\small = \max(\frac{1.617}{\log_2{\!2}}, \frac{{2.959}}{\log_2{\!3}}, \frac{3.202}{\log_2{\!5}}) \;  \\ \small \quad \min(\frac{1.617}{\log_2{\!2}}, \frac{{2.959}}{\log_2{\!3}}, \frac{3.202}{\log_2{\!5}}) \\ \small \approx \max(1.617, {1.867}, 1.379) \;  \\ \small \quad \min(1.617, {1.867}, 1.379) \\ \small = 1.617  {1.867} \\ \small = 3.484[/math]  
[math]\small \begin{align} &\text{ElilsC}(\textbf{i}) \\ &= ‖ ZL\textbf{i} ‖_2 \\ \small &= ‖ \; [ \; L\textbf{i} \;  \; \smallLLzigzag L\textbf{i} \smallRRzigzag_1 \; ] \; ‖_2 \\ \small &= \sqrt{ \sum\limits_{n=1}^d\log_2{\!p_n} · \mathrm{i}_n^2 + {\large}\sum\limits_{n=1}^d(\log_2{\!p_n} · \mathrm{i}_n){\large}^2 } \\ \small &= \sqrt{\strut \log_2{\!p_1} · \mathrm{i}_1^2 + \log_2{\!p_2} · \mathrm{i}_2^2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d^2) \; +} \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \\ \small & \quad\quad\quad \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{{\large}(\log_2{\!p_1} · \mathrm{i}_1) + (\log_2{\!p_2} · \mathrm{i}_2) + \ldots + (\log_2{\!p_d} · \mathrm{i}_d){\large}^2} \end{align}[/math]  [math]\small \begin{align} &= \sqrt{\strut \log_2{\!2} · 1^2 + \log_2{\!3} · {2}^2 + \log_2{\!5} · 1^2 \; +} \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \\ \small & \quad\quad\quad \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{{\large}(\log_2{\!2} · 1) + (\log_2{\!3} · {2}) + (\log_2{\!5} · 1){\large }^2} \\[5pt] \small &= \sqrt{\strut 1^2 + 3.170^2 + 2.322^2 + 1 + 3.170 + 2.322^2} \\ \small &= \sqrt{\strut 1 + 10.048 + 5.391 + 0.023} \\ \small &= \sqrt{\strut 16.462} \\ \small &\approx 4.057 \end{align}[/math]  (unsure; handled via matrix augmentations)  
[math]\begin{align} \small &\text{lolsC}(\dfrac{n}{d}) \\ &= \log_2(\max(&&\text{rough}(n, 3), \\ &&&\text{rough}(d, 3))^2) \end{align}[/math]  [math]\small = \log_2(\max(5, 9)^2) \\ \small = \log_2{\!9·9} \\ \small = \log_2{\!81} \\ \small \approx 6.340[/math]  
[math]\small \text{lolsC}(\dfrac{n}{d}) \\ = \log_2{\!\text{rough}(nd, 3)} + \log_2{\!\frac{\text{rough}(n,3)}{\text{rough}(d,3)}}[/math]  [math]\small = \log_2(5 · 9) + \log_2(\frac59) \\ \small \approx 5.492 + {0.848} \\ \small = 6.340[/math]  [math]\small \begin{align} &\text{lolsC}(\textbf{i}) \\ &= \sum\limits_{n=2}^d\log_2{\!p_n} · \mathrm{i}_n + {\large}\sum\limits_{n=2}^d(\log_2{\!p_n} · \mathrm{i}_n){\large} \\ \small &= \log_2{\!p_2} · \mathrm{i}_2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d \; + \\ \small & \quad\quad\quad {\large}(\log_2{\!p_2} · \mathrm{i}_2) + \ldots + (\log_2{\!p_d} · \mathrm{i}_d){\large} \end{align}[/math]  [math]\small \begin{align} &= \log_2{\!3} · {2} + \log_2{\!5} · 1 \; + \\ \small & \quad\quad\quad {\large}(\log_2{\!3} · {2}) + (\log_2{\!5} · 1){\large} \\ \small &\approx 3.170 + 2.322 + {3.170} + 2.322 \\ \small &= 6.340 \end{align}[/math]  (unsure; handled via matrix augmentations)  
[math]\small \begin{align} &\text{ElolsC}(\textbf{i}) \\ &= \sqrt{ \sum\limits_{n=2}^d\log_2{\!p_n} · \mathrm{i}_n^2 + {\large}\sum\limits_{n=2}^d(\log_2{\!p_n} · \mathrm{i}_n){\large}^2 } \\ \small &= \sqrt{\strut \log_2{\!p_2} · \mathrm{i}_2^2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d^2) \; +} \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \\ \small & \quad\quad\quad \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{{\large}(\log_2{\!p_2} · \mathrm{i}_2) + \ldots + (\log_2{\!p_d} · \mathrm{i}_d){\large}^2} \end{align}[/math]  [math]\small \begin{align} &= \sqrt{\strut \log_2{\!3} · {2}^2 + \log_2{\!5} · 1^2 \; +} \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[11pt]{0pt}{0pt} } \\ \small & \quad\quad\quad \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{\rule[12pt]{0pt}{0pt} } \hspace{1mu} \overline{{\large}(\log_2{\!3} · {2}) + (\log_2{\!5} · 1){\large}^2} \\[5pt] \small &\approx\sqrt{\strut {3.170}^2 + 2.322^2 + {3.170} + 2.322^2} \\ \small &= \sqrt{\strut 10.048 + 5.391 + 0.719} \\ \small &= \sqrt{\strut 16.158} \\ \small &= 4.020 \end{align}[/math]  (unsure; handled via matrix augmentations)  
[math]\small \text{prodC}(\dfrac{n}{d}) \\ = nd[/math]  [math]\small = 10·9 \\ \small = 90[/math]  [math]\small \begin{align} &\text{prodC}(\textbf{i}) \\ &= \prod\limits_{n=1}^d{p_n^{\mathrm{i}_n}} \\ \small &= p_1^{\mathrm{i}_1} × p_2^{\mathrm{i}_2} × \ldots × p_d^{\mathrm{i}_d} \end{align}[/math]  [math]\small = 2^{1} × 3^{{2}} × 5^{1} \\ \small = 2 + 9 + 5 \\ \small = 16[/math]  
[math]\small \begin{align} &\text{sopfrC}(\dfrac{n}{d}) \\ &= \text{sopfr}(nd) \\ \small &= \text{sopfr}(n) + \text{sopfr}(d) \end{align}[/math]  [math]\small = \text{sopfr}(10·9) \\ \small = \text{sopfr}(2·5·3·3) \\ \small = 2^1+ 5^1 + 3^1 + 3^1 \\ \small = 13[/math]  [math]\small \begin{align} &\text{sopfrC}(\textbf{i}) \\ &= ‖\text{diag}(𝒑)\textbf{i}‖_1 \\ \small &= \sqrt[1]{\strut \sum\limits_{n=1}^dp_n · \mathrm{i}_n^1 } \\ \small &= \sqrt[1]{\strut p_1 · \mathrm{i}_1^1 + p_2 · \mathrm{i}_2^1 + \ldots + p_d · \mathrm{i}_d^1} \\ \small &= p_1\mathrm{i}_1 + p_2\mathrm{i}_2 + \ldots + p_d\mathrm{i}_d \end{align}[/math]  [math]\small = 2 · 1 + 3 · {2} + 5 · 1 \\ \small = 2 + 6 + 5 \\ \small = 13[/math]  [math]\small \begin{align} &\text{sopfrC}^{*}(\textbf{𝒓}) \\ &= ‖𝒓\,\text{diag}(𝒑)^{1}‖_∞ \\ \small &= \sqrt[∞]{\strut \sum\limits_{n=1}^d \frac{r_n}{p_n}^∞} \\ \small &= \sqrt[∞]{\strut \frac{r_1}{p_1}^∞ + \frac{r_2}{p_2}^∞ + \ldots + \frac{r_d}{p_d}^∞} \\ \small &= \max(\frac{r_1}{p_1}, \frac{r_2}{p_2}, \ldots, \frac{r_d}{p_d}) \end{align}[/math]  [math]\small = \max(\frac{1.617}{2}, \frac{{2.959}}{3}, \frac{3.202}{5}) \\ \small \approx \max(0.809, 0.986, 0.640) \\ \small = 0.986[/math]  
[math]\small \begin{align} &\text{EsopfrC}(\textbf{i}) \\ &= ‖\text{diag}(𝒑)\textbf{i}‖_2 \\ \small &= \sqrt[2]{\strut \sum\limits_{n=1}^d p_n · \mathrm{i}_n^2} \\ \small &= \sqrt[2]{\strut p_1 · \mathrm{i}_1^2 + p_2 · \mathrm{i}_2^2 + \ldots + p_d · \mathrm{i}_d^2} \\ \small &= \sqrt{\strut (p_1\mathrm{i}_1)^2 + (p_2\mathrm{i}_2)^2 + \ldots + (p_d\mathrm{i}_d)^2} \end{align}[/math]  [math]\small = \sqrt{\strut 2 · 1^2 + 3 · {2}^2 + 5 · 1^2} \\ \small = \sqrt{\strut 2^2 + 6^2 + 5^2} \\ \small = \sqrt{\strut 4 + 36 + 25} \\ \small = \sqrt{\strut 65} \\ \small \approx 8.062[/math]  [math]\small \begin{align} &\text{EsopfrC}^{*}(𝒓) \\ &= ‖𝒓\,\text{diag}(𝒑)^{1}‖_2 \\ \small &= \sqrt[2]{\strut \sum\limits_{n=1}^d \frac{r_n}{p_n}^2} \\ \small &= \sqrt[2]{\strut \frac{r_1}{p_1}^2 + \frac{r_2}{p_2}^2 + \ldots + \frac{r_d}{p_d}^2} \\ \small &= \sqrt{\strut (\frac{r_1}{p_1})^2 + (\frac{r_2}{p_2})^2 + \ldots + (\frac{r_d}{p_d})^2} \end{align}[/math]  [math]\small = \sqrt{\strut (\frac{1.617}{2})^2 + (\frac{{2.959}}{3})^2 + (\frac{3.202}{5})^2} \\ \small \approx \sqrt{\strut 0.654 + 0.973 + 0.410} \\ \small = \sqrt{\strut 2.037} \\ \small \approx 1.427[/math] 
In the next section of this article, we will be taking a look at each of these complexities individually. Formulas, examples, and associated tuning schemes can be found in the reference table above, and below we'll go through each complexity in detail, disambiguating any variable names in the table, suggesting helpful ways to think about the relationships between their formulas, and explaining why some theorists may favor them for regular temperament tuning. Before we begin to look at the complexity functions which we haven't discussed before, though, we'll first be reviewing in this level of detail those few of them that we have already used. This will help us set the stage properly for the newer ones. We'll actually start with the logproduct complexity here, that is, we'll look at it before the product complexity, even though the product complexity is conceptually simpler than it, and technically came up first in our article series. We're doing this for similar reasons to why we adopted the logproduct complexity as our default complexity in all our theory thus far: it's easier to work with given our LA setup for RTT.
This section is structured so that we follow each function's discussion with a discussion of its Euclideanized version, so we end up with an alternating pattern of original and Euclideanized function discussions.
We've adopted the notational choice for functions which are getting applied for computing complexity by suffixing them with [math]\text{C}[/math]. For example, [math]\text{sopfr}()[/math] is simply the mathematical function "sum of prime factors with repetition", while [math]\text{sopfrC}()[/math] is that applied for computing complexity. And any such complexity function has a corresponding simplicity function which returns its reciprocal values, and which is notated instead with the [math]\text{S}[/math] suffix.
Previously we've referred to a norm form of a complexity as having a dual norm as well as an inverse prescaler. For the remainder of this article, we will be switching nomenclature to inverse pretransformer for the latter, on account of the fact that some of the alternative complexity functions described here have norm forms which use a nonrectangular matrix in that position, and so it would be somewhat of a misnomer to consider them merely prescalers in general.
As a final note before we begin: for purposes of this discussion, a "vectorbased form" of a complexity's formula refers specifically to the primecount vectors that we use for JI ratios; the notion of a "vectorbased form" of a mathematical function defined for quotients, i.e. in terms of a numerator and a denominator, has no meaning unless the nature of that vector is specified, and in our case we specify it as a vectorization of the quotients' prime factorization. So for purposes of this article, when we see the quotient [math]\frac{n}{d}[/math], this refers to the same interval as the vector [math]\textbf{i}[/math].
Logproduct
Logproduct complexity [math]\text{lpC}()[/math] was famously used by Paul Erlich in the simplicityweight damage of the "Tenney OPtimal" tuning scheme he introduced in his Middle Path paper, though he referred to it there as "harmonic distance", that being James Tenney's term for it, and Tenney being the first person to apply it to tuning theory (indeed this function is still known to some theorists as "Tenney height"). We still consider this tuning scheme to be the gold standard among the allinterval tuning schemes, and this reflects in how our tuning scheme naming system gives it the simplest name it is capable of giving: "minimaxS". The use of [math]\text{lpC}()[/math] for the minimaxS tuning scheme is discussed at length in the previous article of this series on allinterval tuning schemes, but it can be used to weight error in ordinary tuning schemes too.
Default status
Logproduct complexity was chosen by us (Douglas and Dave) to be our default complexity function. By this it is meant that if one says "miniaverageC" tuning, the assumption is that the complexityweight damage is logproduct complexity; there is no need to spell out "miniaveragelpC". It was chosen as the default for its excellent balance of easytounderstand and easytocompute qualities, while doing a good job capturing the reality of harmonic complexity. For a more indepth defense of this choice and exploration of other possibilities, see the later section A defense of our choice of log product as our default complexity.
Formulas
The quotient form of the logproduct complexity function is the base2 logarithm of the product of a quotient's numerator and denominator:
[math]\text{lpC}(\frac{n}{d}) = \log_2(n·d)[/math]
Or, equivalently, the sum of the base2 logarithm of each separately:
[math]\text{lpC}(\frac{n}{d}) = \log_2(n) + \log_2(d)[/math]
The equivalent vectorbased form of the logproduct complexity function, to be called on the equivalent vector [math]\textbf{i}[/math], is given as a norm by:
[math]\text{lpC}(\textbf{i}) = ‖L\textbf{i}‖_1[/math]
where [math]L[/math] is the logprime matrix, a diagonalized list of the base2 logs of each prime.
The vectorbased form may also be understood as a summation:
[math]
\begin{align}
\text{lpC}(\textbf{i}) &= \sqrt[1]{\strut \sum\limits_{n=1}^d \log_2{\!p_n}\mathrm{i}_n^1} \\
&= \sum\limits_{n=1}^d \log_2{\!p_n}\mathrm{i}_n
\end{align}
[/math]
where [math]p_n[/math] is the [math]n[/math]^{th} prime (assuming this is a temperament of a standard domain, i.e. its basis is a prime limit; in general [math]p_n[/math] is the [math]n[/math]^{th} entry of whichever prime list [math]𝒑[/math] your temperament is working with (or in the general case a basis element list). We also note that [math]n[/math] is not the numerator and [math]d[/math] is not the denominator here; their reappearance together is a coincidence. What [math]n[/math] is here is a generic summation index that increments each step of the sum (matching the [math]n[/math]^{th} prime up with the [math]n[/math]^{th} entry of [math]\textbf{i}[/math]), and [math]d[/math] is the dimensionality of [math]\textbf{i}[/math] (and the temperament in general).
Either way of writing this vectorbased form — norm, or summation — may be expanded to:
[math]
\text{lpC}(\textbf{i}) = \log_2{\!p_1} · \mathrm{i}_1 + \log_2{\!p_2} · \mathrm{i}_2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d
[/math]
In order to understand how the vectorbased form is equivalent to the quotient form, please refer to our explanation using the logarithmic identities [math]\log(a·b) = \log(a) + \log(b)[/math] and [math]\log(a^c) = c·\log(a)[/math] in the allinterval tuning schemes article.
Proportionality to size
Because the logprime matrix [math]L[/math] can also be understood to figure into how we compute the sizes of intervals in cents, we see an interesting effect for tunings that use complexities such as [math]\text{lpC}()[/math] which use it as (or as part of) their complexity pretransformer [math]X[/math]. A complexity pretransformer's inverse pretransformer [math]X^{1}[/math], appears in the minimization target according to RTT's take on the dual norm inequality: [math]‖𝒓X^{1}‖_{\text{dual}(q)}[/math]. So given the following:
 The retuning map is the tuning map minus the just tuning map: [math]𝒓 = 𝒕  𝒋[/math]
 The just tuning map can be understood to be the logprime matrix leftmultiplied by a summation map [math]\slant{\mathbf{1}}[/math] = ⟨1 1 1 …] and then scaled to cents with an octavetocents conversion factor of 1200, that is: [math]𝒋 = 1200×\slant{\mathbf{1}}L[/math]
 In [math]\text{lpC}()[/math] it is the case that [math]X = L[/math].
Then we find:
[math]
‖𝒓X^{1}‖_{\text{dual}(q)}
[/math]
Substituting in for [math]𝒓 = 𝒕  𝒋[/math] and [math]X^{1} = X^{1} = (L)^{1}[/math]
[math]
‖(𝒕  𝒋)(L^{1})‖_{\text{dual}(q)}
[/math]
Distributing:
[math]
‖𝒕L^{1}  𝒋L^{1}‖_{\text{dual}(q)}
[/math]
Substituting in for [math]𝒋 = 1200×\slant{\mathbf{1}}L[/math]:
[math]
‖𝒕L^{1}  (1200×\slant{\mathbf{1}}L)L^{1}‖_{\text{dual}(q)}
[/math]
And now canceling out the [math]LL^{1}[/math] on the second term:
[math]
‖𝒕L^{1}  1200×\slant{\mathbf{1}}\cancel{L}\cancel{L^{1}}‖_{\text{dual}(q)}
[/math]
Note that [math]𝒕 = 1200×\slant{\mathbf{1}}LGM[/math], and so the left term could also be expressed as [math]1200×\slant{\mathbf{1}}LGML^{1}[/math], but [math]L[/math] and [math]L^{1}[/math] do not cancel here, because they are not immediate neighbors. We meaningfully transform into [math]L^{1}[/math] space, deal with [math]M[/math] and [math]G[/math] there, and then transform back out with [math]L[/math] here. So we find that the retuning magnitude is:
[math]
‖𝒕L^{1}  1200×\slant{\mathbf{1}}‖_{\text{dual}(q)}
[/math]
And so this is the minimization target for tuning schemes whose complexity pretransformer equals [math]L[/math]. What's cool about this is how we're tuning each prime proportionally to its own size. The second term here is a map consisting of a string of 1200's, one for each prime. And the first term is the tuning map, but with each term scaled proportionally to the log of the corresponding prime, such that if the tempered version of the prime were to be pure, it would equal 1200 here. If we took the 1200 out of the picture, we could perhaps see even more clearly that we were weightingeach prime proportionally to its own size. In other words, since we're minimizing the difference between each prime's tuning and 1, this would mean that a perfect tuning equals 1, narrow tunings are values like 0.99, and wide tunings are values like 1.01. This is in contrast to the nonpretransformed case, where every cent off from any prime makes the same impact to its difference with its just size.
We could think of it this way: while [math]𝒓[/math] simply gives us the straight amount of error to each prime interval, [math]𝒓L^{1}[/math] gives us that error relative to the size of each of those intervals, i.e in terms of a proportion such as could be expressed with a percentage. In other words, since the interval [math]\frac71[/math] is much bigger than the interval [math]\frac21[/math], if they both had the same amount of error, then that'd actually be quite a bit less error per octave for prime 7 (specifically, [math]\frac{1}{\log_2{7}}[/math] as much). So, if we're using an optimizer to minimize a norm of a vector containing this error value and others, by reducing it in this way, we are telling the optimizer — for each next prime bigger than the last — worry about its error proportionally less than the prime before. This is the rationale Paul used when basing his tuning scheme on a Tenney lattice, and we think that if you're going to use an allinterval tuning, then this is a pretty darn appealing reason to use [math]\text{lp}[/math] as the interval complexity function in the simplicityweighting of your damage. As with nonallinterval tuning schemes, we consider [math]\text{lp}[/math] to be the clear default interval complexity function; this is just a new perspective on that same old intuition.
We might also call attention to the fact that cents and octaves are both logarithmic pitch units. Which is to say that we could simplify the units of such quantities down to nothing if we wanted. That is, we know the conversion factor between these two units, so multiplying by 1/1200 octaves/cent would cancel out the units, leaving us with a dimensionless quantity. So why do we keep these values in the more complicated units of cents per octave? The main reason is convenient size. When tuning temperaments, we tend to deal with smallish damage values: usually less than 10, almost always less than 100. If that gets divided by 1200 (~1000) then we'd typically be comparing amounts in the hundredths. Who wants to compare damages of 0.001 and 0.002, i.e. essentially proportions of octaves? As Graham puts it in his paper Prime Based Error and Complexity Measures, "Because the dimensionless error is quite a small number, using cents per octave instead is a good practical idea."^{[1]}
This [math]L[/math]canceling proportionality effect occurs for allinterval tunings that use any complexity with [math]L[/math] as its pretransformer (or a component thereof), such as minimaxES, minimaxlilsS, and minimaxElilsS described later in this article.
This effect does not occur for ordinary tunings with simplicity weight damage, i.e. where the simplicity matrix is [math]S = \dfrac1L = L^{1}[/math]. At least, it doesn't whenever they use anything other than the primes as targetintervals, i.e. [math]\mathrm{T} ≠ I[/math], because having [math]\mathrm{T}[/math] sandwiched between the [math]L[/math] and the [math]L^{1}[/math] prevents them from canceling:
[math]
\llzigzag \textbf{d} \rrzigzag _p = \llzigzag 1200×\slant{\mathbf{1}}LGM\mathrm{T}L^{1}  1200×\slant{\mathbf{1}}L\mathrm{T}L^{1} \, \rrzigzag _p
[/math]
We'll briefly look at a units analysis of this situation. Interestingly, even though this [math]LL^{1}[/math] cancels out mathematically, the units do not. That's because these really aren't the same [math]L[/math], conceptually speaking anyway. They have the same shape and entries, but different purposes. The first one is there to convert prime counts into octave amounts, i.e. from frequency space where we multiply primes to pitch space where we add their logs; this [math]L[/math] logically has units of oct/p. The second one is there to scale prime factors according to a particular idea of interval complexity, which happens to be (well, we'd say, was engineered to be) in direct correlation with their octave amounts; this pretransformer [math]L[/math] has no actual units but carries a unit annotation (C) that it applies to whatever it pretransforms (and [math]L^{1}[/math] thereby has the annotation (S), for simplicity, being the inverse of [math]L[/math]).^{[2]} And so when we consider the whole expression [math]1200×\slant{\mathbf{1}}LL^{1}[/math], we find that [math]1200[/math] has units of ¢/oct, [math]\slant{\mathbf{1}}[/math] has units of oct/oct, [math]L[/math] has units of oct/p, and [math]L^{1}[/math] has units of (S), and when everything's done canceling out, you end up with ¢/p(S). Note that the annotation is not "in the denominator"; it applies equally to the entire unit. And the most logical way to read this would be as if the annotation was on the numerator, in fact, as "simplicityweighted cents per prime". You can check that this is the same units you'd get for the other term [math]𝒕L^{1}[/math], and thus for their difference, [math]𝒓L^{1}[/math]. Since the noteworthy thing here was that the matched [math]L[/math]'s had no effect on the units analysis, we further note that this analysis is valid for any inverse pretransformed retuning map. But we should also say that it's not entirely wrong to think of [math]L^{1}[/math] as having units of p/oct, i.e. as being the inverse of the [math]L[/math] that's used for sizing interval vectors, in which case we end up with ¢/oct units instead; both Keenan Pepper (in his tiptop.py script) calls the units of damage in minimaxS tuning "cents per octave", and Graham Breed considers them cents per octave as well.^{[3]}
Euclideanized logproduct
To Euclideanize logproduct complexity, we simply take the powers and roots of [math]1[/math] from the vectorbased forms (norm, summation, and expanded) and swap them out for [math]2[/math]'s. This also changes its name from "(taxicab) logproduct complexity" to "Euclideanized" same. Note that the quotient form does not have these powers and thus we have no quotient form of [math]\text{ElpC}()[/math].
[math]
\begin{array} {rcl}
{\color{red}\text{(t)}}\text{lpC} & \text{name} & {\color{red}\text{E}}\text{lpC} \\
\log_2(n·d) & \text{quotient} & \text{ — } \\
‖L\textbf{i}‖_{\color{red}1} & \text{norm} & ‖L\textbf{i}‖_{\color{red}2} \\
\sqrt[{\color{red}1}]{\strut \sum\limits_{n=1}^d \log_2{\!p_n}\mathrm{i}_n^{\color{red}1}} & \text{summation} & \sqrt[{\color{red}2}]{\strut \sum\limits_{n=1}^d \log_2{\!p_n}\mathrm{i}_n^{\color{red}2}} \\
\sqrt[{\color{red}1}]{\strut \log_2{\!p_1} · \mathrm{i}_1^{\color{red}1} + \log_2{\!p_2} · \mathrm{i}_2^{\color{red}1} + \ldots + \log_2{\!p_d} · \mathrm{i}_d^{\color{red}1}} & \text{expanded} & \sqrt[{\color{red}2}]{\strut \log_2{\!p_1} · \mathrm{i}_1^{\color{red}2} + \log_2{\!p_2} · \mathrm{i}_2^{\color{red}2} + \ldots + \log_2{\!p_d} · \mathrm{i}_d^{\color{red}2}}
\end{array}
[/math]
Tunings used in
This complexity was introduced to tuning theory by Graham Breed, for his TenneyEuclidean tuning scheme (our "minimaxES").
MinimaxES is also sometimes referred to as the "T2" tuning scheme, in recognition of its falling along a continuum of tunings that include minimaxS ("TOP"), or "T1" tuning. The '1' and '2' refers to the norm power, so in "T1" the '1' refers to the ordinary case of taxicab norm, and in "T2" the '2' refers to the Euclideanization (and, theoretically, "T3" would refer to an analogous tuning that used a [math]3[/math]norm instead, etc.). Unfortunately, because the meaning of the 'T' is "Tenney", a reference to his harmonic lattice which uses a harmonic distance measure that is a pretransformed 1norm, this is not the cleanest terminology, since any value other than '1' here is overriding that aspect of the meaning of the 'T'. We prefer our systematic naming, which uses an optional "t" (for taxicab) or an "E" (for Euclidean) in place of special norm power values of '1' and '2', respectively, and otherwise just uses the number, for example "T3" would be "minimax3S".
Product
Product complexity is found as the product of a quotient's numerator and denominator:
[math]\small \text{prodC}(\dfrac{n}{d}) = nd[/math]
It doesn't get any simpler than that.
Vectorifiable, but not normifiable
We are able to express product complexity in vectorbased form. However, unlike every other one of the complexities we look at in this article, it does not work out as a summation; rather, it works out as a product, using the analogous but lessfamiliar BigPi notation instead of BigSigma notation ('Π' for 'P' for "product"; 'Σ' for 'S' for "summation").
[math]\small \text{prodC}(\textbf{i}) = \prod\limits_{n=1}^d{p_n^{\mathrm{i}_n}}[/math]
Which expands like this:
[math]p_1^{\mathrm{i}_1} × p_2^{\mathrm{i}_2} × \ldots × p_d^{\mathrm{i}_d}[/math]
For this same reason — being a product, not a summation — product complexity has no way to be expressed as a norm, and so cannot be used directly for allinterval tuning schemes. And this is one of the key reasons why it hasn't seen as much use in tuning theory as its logarithmic cousin, [math]\text{lpC}()[/math]. The logarithmic version exhibits that [math]L[/math]canceling effect (which also underpins Paul's trick for computing minimaxS tunings of nullity1 temperaments, per Dave Keenan & Douglas Blumeyer's guide to RTT: allinterval tuning schemes#Bonus trick: Paul's method for nullity1 minimaxS), and also we believe accounts for it simply "feeling about right" to the typical tuning theorist we've discussed with. Logproduct complexity feels like it was designed expressly for measuring complexity of relative pitch, i.e. pitch deltas, whereas product complexity feels better suited for measuring complexity of relative frequency, i.e. frequency qoppas.^{[4]} For more information on this problem, please see the later section Systematizing norms.
Tunings used in
Curiously, even though [math]\text{prodC}()[/math] cannot be normified, and if it can't be a norm then there can't be a dual norm for it, which means we cannot directly create an allinterval tuning "minimaxprodS" which minimaxes the productsimplicityweight damage across all intervals, we can nevertheless achieve this (if one feels like experimenting with a sort of conceptual mismatch between frequency and pitch, in terms of complexity measurement), albeit it comes with a further step of indirection. This is because of a special relationship between product complexity [math]\text{prodC}()[/math] and sumofprime factorswithrepetition complexity [math]\text{sopfrC}()[/math]: when sopfrsimplicityweight damage is minimaxed, as is done in the minimaxsopfrS tuning, it turns out that simultaneously the prodsimplicityweight damage is also minimized. This is the reason why the historical name for minimaxsopfrS has been "BOP", short for "Benedetti OPtimal", where "Benedetti height" is an alternative name for product complexity, even though minimaxsofprS is computed not with the product complexity but with sumofprimefactorswithrepetition complexity. We'll explain this in the later section on sopfr.
Euclideanized product
This is a trick section, here only for parallelism. There is no Euclideanized product complexity [math]\text{“EprodC”}()[/math]. Despite product complexity boasting a vector form, that vectorbased form involves a product, not a summation, and therefore cannot be expressed as a norm, and Euclideanization is something that can only be done to a norm (or equivalent summation). Compare:
[math]
\begin{array} {rcl}
\sqrt[{\color{red}1}]{\strut \sum\limits_{n=1}^d \log_2{\!p_n}\mathrm{i}_n^{\color{red}1}} & {\color{red}\text{(T)}}\text{lpC} → {\color{red}\text{E}}\text{lpC} & \sqrt[{\color{red}2}]{\strut \sum\limits_{n=1}^d \log_2{\!p_n}\mathrm{i}_n^{\color{red}2}} \\
\prod\limits_{n=1}^d \log_2{\!p_n}^{\mathrm{i}_n} & {\color{red}\text{(T)}}\text{prodC} → \text{“}{\color{red}\text{E}}\text{prodC”} & {\Large\quad\quad\text{?}} \\
\end{array}
[/math]
We simply don't have the matching power and root of a norm to work with here. Since this complexity function does not exist, of course, there are no tuning schemes which use it.
Sumofprimefactorswithrepetition
The "sum of prime factors with repetition" function, due to its ungainly sixwordlong name, is usually initialized to [math]\text{sopfr}()[/math], in all lowercase, and curiously missing the 'w', though perhaps that's for easier comparison with its close cousin function, the "sum of prime factors" (without repetition), which is notated as [math]\text{sopf}()[/math]. And "sopfr" is a dam site more pronounceable than "sopfwr" would be. By "repetition" here it is meant that if a prime factor appears more than once, it is counted for each appearance. It may seem obvious to count all the occurrences of each prime, but there are valuable applications to the withoutrepetition version of this function (though it's unlikely that it will see much use in tuning theory).
The quotient form of sumofprimefactorswithrepetitioncomplexity [math]\text{sopfrC}(\frac{n}{d})[/math] is simply the sumofprimefactorswithrepetition of the numerator times the denominator, [math]\text{sopfr}(nd)[/math], or, equivalently, the [math]\text{sopfr}()[/math] for each of the numerator and denominator separately then added together [math]\text{sopfr}(n) + \text{sopfr}(d)[/math].^{[5]}
The vectorbased form of [math]\text{sopfrC}[/math] looks like so:
[math]
\text{sopfrC}(\textbf{i}) = \sum\limits_{n=1}^d(p_{n}·\mathrm{i}_{n})
[/math]
Where, as we've been doing so far, [math]p_n[/math] is the [math]n[/math]^{th} entry of [math]𝒑[/math], the list of primes that match with the counts in each entry [math]\mathrm{i}_n[/math] of the interval vectors [math]\textbf{i}[/math].
Variations
The tuning theorist Carl Lumma has advocated^{[6]} for a variation on this tuning where the primes are all squared before prescaling. This is supported in the RTT Library in Wolfram Language. In fact, it also supports pretransforming by [math]p^a·\log_2{p}^b[/math], for any power [math]a[/math] or [math]b[/math].
Tunings used in
[math]\text{sopfrC}()[/math] is used in the minimaxsopfrS tuning scheme, historically known as "BOP". As explained above, this tuning is named "Benedetti OPtimal" because "Benedetti" is associated with product complexity — not the sopfr complexity we use as our damage weight when computing it. Apparently more theorists care about minimizing product complexity than about minimizing sopfr, which is fine. We have to minimize with [math]\text{sopfrC}()[/math] weight instead of [math]\text{prodC}()[/math] weight because [math]\text{prodC}()[/math] is not normifiable, being a product, not a summation. And minimizing with [math]\text{sopfrC}()[/math] works because we have a proof that if sopfrSweight damage is minimized, then so too is prodSweight damage. What follows is an adaptation of the proof found here BOP tuning#Proof of Benedetti optimality on all rationals.
By the dual norm inequality, we have:
[math]
\dfrac{𝒓\textbf{i}}{‖\text{diag}(𝒑)\textbf{i}‖_1} \leq ‖𝒓\text{diag}(𝒑)^{1}‖_∞
[/math]
This numerator on the left is the absolute error of an arbitrary interval [math]\textbf{i}[/math] according to some temperament's retuning map [math]𝒓[/math]. And the denominator on the left is equivalent to the sopfr complexity. Dividing by complexity is the same as multiplying by simplicity. And so the entire lefthand side is the sopfrSweight damage to an arbitrary interval. We can therefore minimize the damage to all such intervals by minimizing the righthand side, since that's the direction the inequality points. And minimizing the righthand side is easier. This much should all be review if you've gone through the allinterval tuning schemes article; we're just taking the same concept that we taught with the logproduct complexity, but here doing it with sopfrcomplexity.
We said the denominator on the left half is [math]\text{sopfrC}(\textbf{i})[/math]. Let's actually substitute that in, for improved clarity in upcoming steps.
[math]
\dfrac{𝒓\textbf{i}}{\text{sopfrC}(\textbf{i})} \leq ‖𝒓\text{diag}(𝒑)^{1}‖_∞
[/math]
Our goal is to show that prodSweight damage across all intervals is also minimized by minimizing the righthand side of this inequality:
[math]
\dfrac{𝒓\textbf{i}}{\text{prodC}(\textbf{i})} \leq ‖𝒓\text{diag}(𝒑)^{1}‖_∞
[/math]
Now that's not so easy. But what we can do instead is show that this damage is less or equal to the lefthand side of the original inequality. Another level of minimization down:
[math]
\dfrac{𝒓\textbf{i}}{\text{prodC}(\textbf{i})} \leq \dfrac{𝒓\textbf{i}}{\text{sopfrC}(\textbf{i})} \leq ‖𝒓\text{diag}(𝒑)^{1}‖_∞
[/math]
We can just take the first chunk of that and eliminate the redundant numerators:
[math]
\dfrac{\cancel{𝒓\textbf{i}}}{\text{prodC}(\textbf{i})} \leq \dfrac{\cancel{𝒓\textbf{i}}}{\text{sopfrC}(\textbf{i})}
[/math]
And then reciprocate both sides (which flips the direction of the inequality symbol):
[math]
\text{prodC}(\textbf{i}) \geq \text{sopfrC}(\textbf{i})
[/math]
So we now just have to prove that the product complexity of any given interval will always be greater or equal than its sopfr complexity. And if we do this, we will have proven that minimaxsopfrS tuning is the same as minimaxprodS tuning. So let's consider an exhaustive set of two cases: either [math]\textbf{i}[/math] is prime, or it is not prime.
 If [math]\textbf{i}[/math] is prime, then [math]\text{prodC}(\textbf{i}) = \text{sopfrC}(\textbf{i})[/math]. This is because with only a single prime factor, the trivial product of it with nothing else, and the trivial sum of it with nothing else, will give the same result.
 If [math]\textbf{i}[/math] is not prime, then we have a single case where [math]\text{prodC}(\textbf{i}) = \text{sopfrC}(\textbf{i})[/math], which is [math]\frac41[/math] because 2×2 = 2+2, but in every other case^{[7]} we find [math]\text{prodC}(\textbf{i}) \gt \text{sopfrC}(\textbf{i})[/math]
And so since
[math]
\text{prodC}(\textbf{i}) \geq \text{sopfrC}(\textbf{i})
[/math]
implies
[math]
\dfrac{𝒓\textbf{i}}{\text{prodC}(\textbf{i})} \leq \dfrac{𝒓\textbf{i}}{\text{sopfrC}(\textbf{i})}
[/math]
and
[math]
\dfrac{𝒓\textbf{i}}{\text{sopfrC}(\textbf{i})} \leq ‖𝒓\text{diag}(𝒑)^{1}‖_∞
[/math]
therefore
[math]
\dfrac{𝒓\textbf{i}}{\text{prodC}(\textbf{i})} \leq ‖𝒓\text{diag}(𝒑)^{1}‖_∞
[/math]
Done.
Euclideanized sumofprimefactorswithrepetition
Not much to see here; only Euclideanized [math]\text{sopfrC}()[/math], which is to say:
[math] \begin{align} \text{sopfrC} &= ‖\text{diag}(𝒑)\textbf{i}‖_{\color{red}1} \\ {\color{red}\text{E}}\text{sopfrC} &= ‖\text{diag}(𝒑)\textbf{i}‖_{\color{red}2} \\ \end{align} [/math]
Tunings used in
As one should expect, [math]\text{EsopfrC}()[/math] is used in the minimaxEsopfrS tuning scheme, historically known as "BE", where the 'B' is for "Benedetti" as in the Benedetti height as in the product complexity (which has that special relationship with the sumofprimefactorswithrepetition complexity), and the 'E' is as in "Euclidean".
Countofprimefactorswithrepetition
The count of prime factors with repetition function is closely related to the sum of prime factors with repetition function, as one might expect considering how their names are off by just this one word. The only difference is that where in [math]\text{sopfr}()[/math] we sum each prime factor, we can think of [math]\text{copfr}()[/math] as doing the same thing except replacing each prime factor — regardless of its size — with the number [math]1[/math].
Tunings used in
The count of prime factors with repetition has not actually been used in a tuning scheme before to our knowledge. However, we've included it here on the possibility that it might be, but mostly for parallelism with the other pairs of taxicab and Euclideanized versions of the otherwise same complexity function, due to the fact that the Euclideanized version of [math]\text{copfrC}()[/math] has seen use.
If this minimaxcopfrS tuning were to be used, a noteworthy property it has is that whenever a temperament has only one comma (i.e. is nullity1) every prime receives an equal amount of absolute error in cents. Compare this with the effect we see for minimaxS tuning as observed by Paul Erlich in his Middle Path paper: Dave Keenan & Douglas Blumeyer's guide to RTT: allinterval tuning schemes#Bonus trick: Paul's method for nullity1 minimaxS.
Euclideanized countofprimefactorswithrepetition
In terms of its formulas, [math]\text{EcopfrC}()[/math] is fairly straightforward: it's just the [math]\text{copfrC}()[/math] but with the powers and roots changed from 1 to 2, so said another way, it's the (unpretransformed) [math]2[/math]norm:
[math]\small \begin{align} \text{EcopfrC}(\textbf{i}) &= ‖\textbf{i}‖_2 \\ \small
&= \sqrt[2]{\strut \sum\limits_{n=1}^d \mathrm{i}_n^2} \\ \small
&= \sqrt[2]{\strut \mathrm{i}_1^2 + \mathrm{i}_2^2 + \ldots + \mathrm{i}_d^2} \\ \small
&= \sqrt{\strut \mathrm{i}_1^2 + \mathrm{i}_2^2 + \ldots + \mathrm{i}_d^2} \\
\end{align} [/math]
Thinking about how this actually ranks intervals, it should become clear soon enough that this is complete garbage as a complexity function. It treats all prime numbers as having equal complexity — the lowest complexity possible — and it treats powers of 2 as the most complex numbers in their vicinity. e.g. It considers 8 to be more complex than 9, 10, 11, 12, 13, 14 and 15. So it's a far cry from monotonicity over the integers, i.e. any higher integer is more complex than the previous one.
Tunings used in
How, you might ask, did such a ridiculouslynamed^{[8]} complexity function, that completely flattens the influence of the different prime factors, and suffers the tuning shortcomings of any Euclideanized tuning scheme of measuring harmonic distance through the nonreal diagonal space of the lattice, come to be used as an error weight in tuning schemes? The answer is: because of the pseudoinverse. It happens to be the case that when the pseudoinverse of the temperament mapping is taken to be the generator embedding, this is equivalent to having optimized for minimizing the [math]\text{EcopfrS}()[/math]weight (note the 'S' for simplicity) damage across all intervals — no target list, no prescaling, no nothing. Why exactly this works out like this is a topic for the later section where we work through the computations of this tuning. For now we will simply note that the historical name of this tuning scheme is "Frobenius", on account of the fact that it also minimizes the Frobenius norm (a matrix norm that generalizes the [math]2[/math]norm we use for vectors) of the projection matrix, while in our systematic name this is simply the minimaxEcopfrS tuning scheme. And we'll say that while this pseudoinverse method is basically the easiest possible method for "optimizing" a tuning that could possibly exist, and was fascinating upon its initial discovery, it does come with the tradeoff of probably not being worth doing in the first place — the ultimate example of streetlight effect in RTT — especially in a modern environment where many tools are available for automatically computing better tunings just as quickly and easily, from the average musician's perspective.
Also of note, this allinterval tuning scheme minimaxEcopfrS is equivalent to an ordinary tuning that would be called, in our system, "primes miniRMSU", even though these have different target interval sets ("all" versus "primes"), optimization powers ([math]∞[/math] versus [math]2[/math]), and damage weight (copfrS versus unity). The reason being that with minimaxEcopfrS, we're just minimizing [math]‖𝒓X^{1}‖_2[/math] where [math]X^{1} = I[/math] and [math]𝒓 = 𝒈M  𝒋[/math], and with primes miniRMSU we're minimizing [math]{\large ⟪}𝒈M  𝒋\mathrm{T}W{\large ⟫}_2[/math] where both [math]\mathrm{T} = I[/math] on account of "primes" and [math]W = I[/math] on account of the unityweight. And it doesn't matter whether we compute the minimum of a power norm [math]‖·‖_q[/math] or a power mean [math]{\large ⟪}·{\large ⟫}_p[/math]; we'll find the same tuning either way. Put another way, with either scheme, we find the optimal tuning as the pseudoinverse of [math]M[/math].
A similar equivalence is found between the minimaxcopfrS tuning scheme and the primes minimaxU tuning scheme; the only difference here is that the optimization power of the latter is [math]∞[/math] which matches with the dual norm power of copfr. And a similar equivalence would also be found between the minimaxMcopfrS tuning scheme (the "maxized" variant) and the primesminisumU tuning scheme, if anyone used maxized variants of complexities.
Logintegerlimitsquared
Before looking at the logintegerlimitsquared complexity, let's first cover the plain integerlimitsquared complexity. Actually, let's just look at the integer limit function. Called on an integer, it would return the integer itself. Called on a rational, well, now we have two integers to look at, so it's going to give us whichever one of the two is greater.
[math]
\text{il}(\frac{n}{d}) = \max(n, d)
[/math]
And in the context of RTT tuning, where tuning a subunison interval is equivalent to tuning its superunison equivalent, we typically normalize to all superunisons, i.e. intervals where the numerator is greater than the denominator, so the integer limit typically means simply to return the numerator and throw away the denominator:
assuming [math]\; n \gt d, \quad \text{il}(\frac{n}{d}) = n[/math]
The integerlimitsquared, then, is typically just the numerator squared. It's as if instead of dropping the denominator, we replaced it with a copy of the numerator.
[math]
\text{assuming} \; n \gt d, \quad \text{ils}(\frac{n}{d}) = n×n = n^2
[/math]
And the log integer limit, then, is just the logarithm of the above:
[math]
{\color{red}\text{l}}\text{ilsC}(\frac{n}{d}) = {\color{red}\log_2(}(\max(n, d))^2{\color{red})}
[/math]
Owing to the base2 logarithmic nature of octaveequivalence in human pitch perception, we tend to go with 2 as our logarithm base, as we've done here.
Why squared?
Why use logintegerlimitsquared rather than simply logintegerlimit? The main reason is: for important use cases of tuning schemes based on this complexity, it simplifies formulas. In short, squaring it here actually eliminates annoying factors of [math]\frac12[/math] elsewhere.
There's also an argument that this makes it more nearly equivalent to logproduct complexity in concept, as they now are both the log of the product of two 'ators; in the logproduct case, it's a numerator and denominator, while in the logintegerlimitsquared case, it's two numerators.
Note that the log of anything squared is the same as two times the log of the original thing. So [math]\log((\max(n,d))^2) = 2×\log(\max(n,d))[/math].
Also note that "logintegerlimitsquared" tuning is parsed as "log" of "integerlimitsquared", not as "logintegerlimit" then "squared."
Normifying: The cancelingout machine
As we've seen many times by now, in order to use this complexity in an allinterval tuning, we need to get it into the form of a norm, so that we can minimize its dual norm. So, how to normify this formula that involves the taking of a maximum value? At first, this may not seem possible. However, theoreticians have developed a clever trick for this. Now this is only the first step to normifying this function; as we will see in a moment, it is indeed still quotientbased. However, we'll also find that this quotientbased formula — since it has gotten rid of the [math]\max()[/math] function which we didn't know how to normify — will eventually be normifiable. Check it out:
[math]\text{lilsC}(\dfrac{n}{d}) = \log_2{\!nd} + \log_2{\!\frac{n}{d}}[/math]
It turns out that the log of the max of the numerator and denominator is always equivalent to the log of their product plus the absolute value of the log of their quotient. On first appearances, this transformation may be completely opaque. To understand how this trick works, let's take a look at the simplest possible version of it, and then adapt that back into our particular use case.
So here's the purest distillation of the idea: [math]2×\max(a, b) = a + b + a  b[/math]. The key thing is to notice what happens with the absolute value bars. Think about it this way: to extract the maximum value between [math]a[/math] and [math]b[/math], we need some way to throw away the smaller of the two values completely, while retaining the greater of the two values exactly as it came in. So, we cleverly leverage the absolute value bars here as a sort of cancelingout machine.
We can prove that trick works by exhaustively checking all three of the possible cases:
 [math]a \gt b[/math]
 [math]a \lt b[/math]
 [math]a = b[/math]
First, let's check [math]a \gt b[/math]. In this case, [math]a  b = a  b[/math], which is to say that we can simply drop the absolute value bars, because we know that the value inside of them is positive. That gives us:
[math]
\begin{align}
\text{if} \; a \gt b, 2×\max(a, b) &= a + b + a  b \\
&= a + b + {\color{red}(a  b)} \\
&= a \cancel{+ b} + a \cancel{ b} \\
&= a + a \\
&= 2a
\end{align}
[/math]
And when [math]a \lt b[/math], then [math]a  b = b  a[/math]. It's always going to be the big one minus the small one. So that gives us:
[math]
\begin{align}
\text{if} \; a \lt b, 2×\max(a, b) &= a + b + a  b \\
&= a + b + {\color{red}(b  a)} \\
&= \cancel{a} + b + b \cancel{ a} \\
&= b + b \\
&= 2b
\end{align}
[/math]
Voilà. We've achieved our cancelingout machine. We can also notice that as a sideeffect of the cancelingout machine we not only preserve the greater of [math]a[/math] and [math]b[/math], but create an extra copy of it. So that's the reason for the factor of [math]2[/math]: to deal with that sideeffect. (Alternatively, we could have looked at the [math]\max(a, b)[/math] and kept a factor of [math]\frac12[/math] on the right side of the equality.)
But just to be careful, let's also check the edge case of [math]a = b[/math]. Here, the stuff inside the absolute value bars goes to zero, and we can substitute [math]a[/math] for [math]b[/math] (or vice versa), so we get:
[math]
\begin{align}
\text{if} \; a = b, 2×\max(a, b) &= a + b + a  b \\
&= a + {\color{red}a} + {\color{red}(0)} \\
&= a + a \\
&= 2a
\end{align}
[/math]
Okay, now that we've proved that [math]2×\max(a, b) = a + b + a  b[/math] we substitute [math]\log_2{\!n}[/math] for [math]a[/math] and [math]\log_2{\!d}[/math] for [math]b[/math] to obtain:
[math]2×\max(\log_2{\!n}, \log_2{\!d}) = \log_2{\!n} + \log_2{\!d} +  \log_2{n}  \log_2{d} [/math]
First notice that the ordering of two numbers is not changed by taking their logarithm. We say that the logarithm function is monotonic. Then recall the logarithmic identities [math]\log{ab} = \log{a} + \log{b}[/math], and [math]\log{\frac{a}{b}} = \log{a}  \log{b}[/math].
[math]
\begin{array} {c}
& 2×\max(\log_2{\!n}, \log_2{\!d}) & = & \log_2{\!n} + \log_2{\!d} & + &  & \log_2{n}  \log_2{d}&  \\
& ↓ & & ↓ & & & ↓ \\
\text{lilsC}(\dfrac{n}{d}) = & \log_2(\max(n, d)^2) & = & \log_2{\!nd} & + &  & \log_2{\!\frac{n}{d}} &  \\
\end{array}
[/math]
Normifying: complexity and size
Alright. So with the above, even though its argument is still in quotient form, we've taken one step towards normification by removing the unnormifiable element of the [math]\max()[/math] function.
We'll take this next step in two parts. Let's first focus in on the first term:
[math]\log_2{\!nd}[/math]
Notice something about this? It's identical to logproduct complexity, [math]\text{lpC}(\frac{n}{d})[/math]. Very cool, if you weren't expecting to find something so familiar in here.
And as for the second term:
[math]\log_2{\!\frac{n}{d}}[/math]
This too has a straightforward interpretation: it is the size of the interval [math]\frac{n}{d}[/math] in logarithmic pitch (specifically, on account of the base2 of this logarithm, in octaves), which we might at least for this isolated context denote as [math]\text{size}(\frac{n}{d})[/math].
Putting these two insights together, we find that the logintegerlimitsquared complexity may be described as the sum of the interval's logproduct complexity and size in octaves:
[math]\text{lilsC}(\dfrac{n}{d}) = \text{lpC}(\frac{n}{d}) + \text{size}(\frac{n}{d})[/math]
And so, we can describe logintegerlimitsquared complexity in comparison with logproduct complexity: it's the same thing, except that it also accounts for the size of the interval. As a tuning theorist, this an exciting idea, and may make one wonder how anyone was okay before with discarding that information, which certainly seems relevant enough to determining the musical importance of JI intervals. But don't get too excited too quickly, because there are tradeoffs.
We can see how [math]\text{lpC}()[/math] throws away the sizerelated information in how it treats the numinator and diminuator indiscriminately. (Apologies for the neologisms, but we find them helpful enough to be worth the offense in this case; hopefully it is apparent enough that the "numinator" is the greater of the numerator and denominator, referencing the word "numinous", while the "diminuator" is the lesser of those two, referencing the word "diminutive".) This can be seen in how both [math]\frac61[/math] and [math]\frac32[/math] have the same logproduct complexity of [math]\log_2{\!6}[/math], with [math]\text{lpC}()[/math] paying no heed to the fact that [math]\frac61[/math] is a larger interval.
On the other hand, [math]\text{lilsC}()[/math] records this sizerelated information, with [math]\frac61[/math] having complexity of [math]\log_2{\!6}[/math] while [math]\frac32[/math] only has complexity of [math]\log_2{\!3}[/math]. However, there's something that [math]\text{lpC}()[/math] makes use of that [math]\text{lilsC}()[/math] does not use at all: the diminuator. Notice how it doesn't matter to [math]\text{lilsC}()[/math] what value we insert in the diminuator, so long as it remains the diminuator, i.e. smaller than the numinator; sure, [math]\frac61[/math] has complexity of [math]\log_2{6}[/math], but so does [math]\frac65[/math], which many tuning theorists are sure to balk at.
Mike Battaglia has proposed a continuum of complexity functions that exist in the space between the these two extreme poles of logproduct [math]\text{lpC}()[/math] and logintegerlimitsquared [math]\text{lilsC}()[/math] complexity. We will discuss these in more detail below, in the #Hybrids between integerlimitsquared and product complexity section.
Normifying: flattened absolute value sums
Alright, well that was some interesting insight into the motivation behind using this complexity for tuning, but we're still not to a normified form!
 In our first pass at normification, we managed to convert the obvious quotient form of [math]\text{lilsC}()[/math] into a form that didn't use the mathematical [math]\max()[/math] function, instead only using operations which we know better how to work with.
 In our second pass at normification, we merely made the observation that this new form of the formula is akin to asking for the average of the interval's complexity and size, for a particular definition of complexity, anyway, that being the logproduct complexity.
 In our third pass at normification here, then, we will show how to leverage this knowledge about complexity and size to attain norm form.
The first step here is to recognize how close we already are. We've identified the first of our two main terms, the one representing complexity, as exactly equivalent to the logproduct complexity, [math]\text{lpC}()[/math]. And notably, we already know the norm form for that: [math]‖L\textbf{i}‖_1[/math]. So we're already about halfway there. Nice.
But what about the other term, the one representing size? Can this be represented as a norm? Well, the short answer is: no, not directly. To understand why, it may be helpful to remind ourselves of the summation form of [math]\text{lpC}(\textbf{i}) = ‖L\textbf{i}‖_1[/math]:
[math]
\text{lpC}(\frac{n}{d}) = \log_2{\!nd} = \sum\limits_{n=1}^d {\color{red}}\log_2{\!p_n} · \mathrm{i}_n{\color{red}}
[/math]
Note in particular the presence of the absolute value bars, highlighted in red. This is exactly what caused the denominator [math]d[/math] of the argument to no longer divide the numerator [math]n[/math], but to multiply it in the [math]\log_2{\!nd}[/math] form. So if we hadn't done that, we'd instead have the summation that appears inside the absolute value bars of the size formula:
[math]
\text{size}(\frac{n}{d}) = \log_2{\!\frac{n}{d}} = \sum\limits_{n=1}^d {\color{red}(}\log_2{\!p_n} · \mathrm{i}_n{\color{red})}
[/math]
The summation here is identical, except that it now lacks the absolute value bars, i.e the ones around each term; we've replaced them here with simple parentheses (of course we can still see some absolute value bars here, but these are different; these are taken of the resultant value of the summation itself in the end, and come from the size formula that uses this summation). In other words, we have this vector [math]\textbf{i}[/math] which is the vectorbased form of the ratio [math]\frac{n}{d}[/math], and it may have some negative entries — for example [math]\frac54[/math] = [2 0 1⟩ — and those negative entries correspond to the prime factors that appear in the denominator. When we absolute value them, it's the same thing as moving them all into the numerator instead, or in other words, changing [math]\frac{n}{d}[/math] into [math]nd[/math], or in our example, changing [math]\frac54[/math] = [2 0 1⟩ into [math]\frac{20}{1}[/math] = [2 0 1⟩.
So what's the problem, then? The problem is that the definition of a norm includes taking the absolute value of each entry of the vector. So we can't change the [math]\text{size}()[/math] itself into a power norm if it does not take the absolute value of each of its entries. We can at least turn it into a power sum, however, and represent it using our doublezigzagbracket notation: [math] \llzigzag L\mathbf{i} \rrzigzag _1[/math].
So, for now, we're stuck with this:
[math]
\begin{array} {c}
\text{lilsC}(\frac{n}{d}) & = & \log_2{\!nd} & + &  & \log_2{\!\frac{n}{d}} &  \\
& & ↓ & & & ↓ \\
& = & \sum\limits_{n=1}^d \log_2{\!p_n} · \mathrm{i}_n & + & {\large} & \sum\limits_{n=1}^d (\log_2{\!p_n} · \mathrm{i}_n) & {\large}\\
& & ↓ & & & ↓ \\
& = & ‖L\textbf{i}‖_1 & + &  & \llzigzag L\mathbf{i} \rrzigzag _1 & 
\end{array}
[/math]
However, there is still a way forward. It's perhaps easiest to see if we get the vectorbased form of the complexity into its most fundamental form: no norms, no summations, just expand every term all the way out. So the expanded form of [math]‖L\textbf{i}‖_1[/math] is:
[math]
\log_2{\!p_1} · \mathrm{i}_1 + \log_2{\!p_2} · \mathrm{i}_2 + \ldots + \log_2{\!p_d} · \mathrm{i}_d
[/math]
When we use this instead in our formula for [math]\text{lilsC}()[/math], we can attain a new insight, as hinted at through the red highlights below:
[math]
\text{lilsC}(\frac{n}{d}) = {\color{red}}\log_2{\!p_1} · \mathrm{i}_1{\color{red} + }\log_2{\!p_2} · \mathrm{i}_2{\color{red} \; + \;} \ldots {\color{red}\; + \; }\log_2{\!p_d} · \mathrm{i}_d{\color{red} + } \llzigzag L\mathbf{i} \rrzigzag _1{\color{red}}
[/math]
Notice how in a way we've flattened things out now. The final term, the one which represents the size of the interval, is just one more item which is an absolute value of something and which is tacked on to a list of things being added together. Therefore, we can reinterpret this formula as if the whole thing is one big [math]1[/math]norm, of a vector which is just like [math]\textbf{i}[/math] except that it also includes one extra entry at the end, whose value is this interval's own size!
We could write this vector using block matrix notation, or in other words, as an augmented vector, like so:
[math]
\left[ \begin{array} {ll} L\textbf{i} & \llzigzag L\mathbf{i} \rrzigzag _1 \end{array} \right]
[/math]
And so here's the entire norm again, so we can see that we have a power sum nested inside the power norm:
[math]
\text{lilsC}(\textbf{i}) = ‖ \; [ \; L\textbf{i} \;  \; \llzigzag L\textbf{i} \rrzigzag _1 \; ] \; ‖_1
[/math]
We certainly acknowledge that this is can be pretty confusing: how we can look at this complexity as a pair of very similar looking summations:
[math]
\text{lilsC}(\textbf{i}) = \sum\limits_{n=1}^d\log_2{\!p_n} · \mathrm{i}_n + {\large}\sum\limits_{n=1}^d(\log_2{\!p_n} · \mathrm{i}_n){\large}
[/math]
And yet when we convert this into the form of a norm, it is not the case that each of these two summations converts into its own norm, but rather the case that the second summation becomes nothing but an extra entry in the vector that the norm form of the first summation is called on. But that's just how it is! Who knows how they came up with this stuff.^{[9]}
Normifying: example
This is fairly tricky, so we'll supplement with an example. Suppose the interval in question is [math]\frac{10}{9}[/math], which as a vector is [math]\textbf{i}[/math] = [1 2 1⟩. Then we have:
[math]
\begin{array} {c}
\text{lilsC}([ \; \textbf{i} \;  \; \text{size}(\textbf{i}) \; ]) & = & \log_2{\!2} · 1 & + & \log_2{\!3} · {2} & + & \log_2{\!5} · 1 & + & (\log_2{\!2} · 1) + (\log_2{\!3} · {2}) + (\log_2{\!5} · 1) & ) \\
& \approx & 1 & + & {3.170} & + & 2.322 & + & 1 + {3.170} + 2.322 \\
& = & 1 & + & 3.170 & + & 2.322 & + & 0.152 \\
\end{array}
[/math]
Which equals [math]6.644[/math], which is good, because that's [math]\approx \log_2{10·10}[/math], which is the log of the numinator of [math]\frac{10}{9}[/math], squared.
You'll notice that in the final term, we see all the same values repeated: the 1, the 3.170, and the 2.322. It's just that here we could informally say that we let each of these values "duke it out", i.e. that we let the negative ones be negative so they counteract each other. Whereas outside this final term, each of the values gets to be positive and express itself. So the inside is the actual size of the interval irrespective of its complexity, and the outside is the complexity of that interval irrespective of its size (all sizes in octaves here, not cents as we typically work with). And then we just add it all up and done.
Normifying: sizesensitizing matrix
While this augmented vector situation works, it's quite tough to read and work with. It would be nice if we had a simpler way to present the same information. Fortunately, we're in luck! There is. For the next step, we will show how the same effect of including the size of an interval as an extra entry at the end can be achieved by introducing a new transformation matrix, the sizesensitizing matrix, [math]Z[/math]. Here's a 5limit example:
[math]
\begin{array} {c}
Z \\
\left[ \begin{array} {r}
1 & 0 & 0 \\
0 & 1 & 0 \\
0 & 0 & 1 \\
\hline
1 & 1 & 1 \\
\end{array} \right]
\end{array}
[/math]
In action, this lets us rewrite:
[math]
\text{lilsC}(\textbf{i}) = ‖ \; [ \; L\textbf{i} \;  \; \llzigzag L\textbf{i} \rrzigzag _1 \; ] \; ‖_1
[/math]
as
[math]
\text{lilsC}(\textbf{i}) = ‖ ZL\textbf{i} ‖_1
[/math]
Excellent! That's a big win. At last we have lilsC as a pretransformed norm, like every other complexity we've seen. Why don't we convince ourselves this works by rerunning it on the example we just tried above, with [math]\frac{10}{9}[/math]:
[math]
\text{lilsC}(\textbf{i}) = {\Huge‖}
\begin{array} {c}
Z \\
\left[ \begin{array} {r}
1 & 0 & 0 \\
0 & 1 & 0 \\
0 & 0 & 1 \\
\hline
1 & 1 & 1 \\
\end{array} \right]
\end{array}
\begin{array} {c}
L \\
\left[ \begin{array} {r}
\log_2{2} & 0 & 0 \\
0 & \log_2{3} & 0 \\
0 & 0 & \log_2{5} \\
\end{array} \right]
\end{array}
\begin{array} {c}
\textbf{i} \\
\left[ \begin{array} {r}
1 \\
{2} \\
1 \\
\end{array} \right]
\end{array}
{\Huge‖}_1
[/math]
Let's actually work through this two different ways. First, we'll do it the way of multiplying [math]L[/math] and [math]\textbf{i}[/math] together first, and seeing how [math]Z[/math] affects that; second, we'll do it the way of multiplying [math]Z[/math] and [math]L[/math] together first, and seeing how that affects [math]\textbf{i}[/math].
[math]
\begin{align}
\text{lilsC}(\textbf{i}) &= {\Huge‖}
\begin{array} {c}
Z \\
\left[ \begin{array} {c}
1 & 0 & 0 \\
0 & 1 & 0 \\
0 & 0 & 1 \\
\hline
1 & 1 & 1 \\
\end{array} \right]
\end{array}
\begin{array} {c}
L\textbf{i} \\
\left[ \begin{array} {c}
\log_2{2} \\
{2}\log_2{3} \\
\log_2{5} \\
\end{array} \right]
\end{array}
{\Huge‖}_1
\\ &=
{\Huge‖}
\begin{array} {c}
ZL\textbf{i} \\
\left[ \begin{array} {c}
\log_2{2} & + & 0 & + & 0 \\
0 & + & {2}\log_2{3} & + & 0 \\
0 & + & 0 & + & \log_2{5} \\
\hline
\log_2{2} & + & {2}\log_2{3} & + & \log_2{5} \\
\end{array} \right]
\end{array}
{\Huge‖}_1
\\ &=
{\Huge‖}
\begin{array} {c}
ZL\textbf{i} \\
\left[ \begin{array} {c}
\log_2{2} \\
{2}\log_2{3} \\
\log_2{5} \\
\hline
\log_2{2} + {2}\log_2{3} + \log_2{5} \\
\end{array} \right]
\end{array}
{\Huge‖}_1
\\ &=
\log_2{2} + {2}\log_2{3} + \log_2{5} + \log_2{2} + {2}\log_2{3} + \log_2{5}
\\ &=
\log_2{2} + \cancel{2\log_2{3}} + \log_2{5} + \log_2{2} + \cancel{{2}\log_2{3}} + \log_2{5}
\\ &=
2\log_2{2} + 2\log_2{5}
\\ &=
2\log_2{10}
\\ &=
\log_2{10^2}
\end{align}
[/math]
Which is good, because that matches what we found previously, and what we expected: the log of the numerator of [math]\frac{10}{9}[/math], squared.
Doing it the other way we see:
[math]
\text{lilsC}(\textbf{i}) = ‖
\begin{array} {c}
ZL \\
\left[ \begin{array} {r}
\log_2{2} & 0 & 0 \\
0 & \log_2{3} & 0 \\
0 & 0 & \log_2{5} \\
\hline
\log_2{2} & \log_2{3} & \log_2{5} \\
\end{array} \right]
\end{array}
\begin{array} {c}
\textbf{i} \\
\left[ \begin{array} {r}
1 \\
{2} \\
1 \\
\end{array} \right]
\end{array}
‖_1
[/math]
So we can say that the complexity pretransformer for the logintegerlimit [math]X = ZL[/math], where the above is an example for the 5limit. (From this point onward, the computation is the same as before).
Tunings used in
The logintegerlimitsquared complexity is used in the minimaxlilsS tuning scheme, which historically has been called "Weil", for André Weil, the de facto early leader of the mathematical Bourbaki group^{[10]}, on account of "Weil height" being another name for "integer limit". This comes to us from Gene Ward Smith^{[11]}, who himself acknowledges the ambiguity in this name between its ordinary and logarithmic forms. We really don't understand how these sorts of obscurings of otherwise straightforward ideas seem to take root so easily in RTT. As a mnemonic to remember that Weil height gives you the (log/notlog) integer limit AKA max of numerator and denominator, you can recall that Maxwell is a real given name, which associates "max" with "well" and "well" is close to "Weil". But how much easier it is, to remember that "lil" stands for "log integer limit". Encode, don't encrypt.
Note that Weil tuning is thought of as being computed with the "Weil height", which is the same as logintegerlimit complexity, not logintegerlimitsquared complexity. These give the same tuning. We prefer squaring because it makes the computation in certain key circumstances easier.
Euclideanized logintegerlimitsquared
There's not much to see here. As with other Euclideanized complexities, we have no quotient form for [math]\text{ElilsC}()[/math], and the vector (norm) form is simply the normal (taxicab) form but with the power and root of 1 swapped for 2:
[math]
\begin{align}
\text{lilsC}(\textbf{i}) &= ‖ \; [ \; L\textbf{i} \;  \; \llzigzag L\textbf{i} \rrzigzag _1 \; ] \; ‖_1 = ‖ \; ZL\textbf{i}\; ‖_1 \\
{\color{red}\text{E}}\text{lilsC}(\textbf{i}) &= ‖ \; [ \; L\textbf{i} \;  \; \llzigzag L\textbf{i} \rrzigzag _1 \; ] \; ‖_{\color{red}2} = ‖ \; ZL\textbf{i}\; ‖_{\color{red}2}
\end{align}
[/math]
Example wonky result
To illustrate some of the wonkiness we can expect with [math]\text{ElilsC}()[/math], we note that it ranks [math]\frac{5}{3}[/math] as less complex than [math]\frac{5}{1}[/math]. Remember that integerlimitbased complexities watch for producttype complexity but also watch for the size of the interval, so we can say that in this case, the size difference between [math]\frac{5}{1}[/math] and [math]\frac{5}{3}[/math] was considered more important than the different in their product complexities, so that [math]\frac{5}{1}[/math] comes out as more complex.
So we have:
[math]
\text{ElilsC}(\textbf{i}) = ‖ \; [ \; L\textbf{i} \; {\large} \; \text{size}(L\textbf{i}) \; ⟩ \; ‖_2
[/math]
Let's compute the size part first, for our first interval, [math]\frac{5}{1}[/math], which is [0 0 1⟩ as a vector:
[math]
\begin{align}
\text{size}(L [ \; 0 \; 0 \; 1 \; ⟩) &= \llzigzag L [ \; 0 \; 0 \; 1 \; ⟩ \rrzigzag _1 \\
&\approx \llzigzag [ \; 0 \;\;\; 0 \;\;\; 2.322 \; ⟩ \rrzigzag _1 \\
&= 0 + 0 + 2.322 \\
&= 2.322
\end{align}
[/math]
So we can use that in the below:
[math]
\begin{align}
\text{ElilsC}( [ \; 0 \; 0 \; 1 \; ⟩ )
&= ‖ \; [ \; L [ \; 0 \; 0 \; 1 \; ⟩ \; {\large} \; \text{size}(L [ \; 0 \; 0 \; 1 \; ⟩) \; ⟩ \; ‖_2 \\
&\approx ‖ \; [ \; 0 \;\;\; 0 \;\;\; 2.322 \;\;\;  \;\;\; 2.322 \; ⟩ \; ‖_2 \\
&= \sqrt{\strut 0^2 + 0^2 + 2.322^2 + 2.322^2} \\
&\approx \sqrt{\strut 0 + 0 + 5.391 + 5.391} \\
&= \sqrt{\strut 10.782} \\
&\approx 3.284 \\
\end{align}
[/math]
And compare with the result for [math]\frac{5}{3}[/math], which is [0 1 1⟩ as a vector. First, its size:
[math]
\begin{align}
\text{size}(L [ \; 0 \; {1} \; 1 \; ⟩) &= \llzigzag L [ \; 0 \; {1} \; 1 \; ⟩ \rrzigzag _1 \\
&\approx \llzigzag [ \; 0 \;\;\; {1.585} \;\;\; 2.322 \; ⟩ \rrzigzag _1 \\
&= 0 + {1.585} + 2.322 \\
&= 0.737
\end{align}
[/math]
And now the rest:
[math]
\begin{align}
\text{ElilsC}( [ \; 0 \; {1} \; 1 \; ⟩ )
&= ‖ \; [ \; L [ \; 0 \; 0 \; 1 \; ⟩ \; {\large} \; \text{size}(L [ \; 0 \; {1} \; 1 \; ⟩) \; ⟩ \; ‖_2 \\
&\approx ‖ \; [ \; 0 \;\;\; {1.585} \;\;\; 2.322 \;\;\;  \;\;\; 0.737 \; ⟩ \; ‖_2 \\
&= \sqrt{\strut 0^2 + {1.585}^2 + 2.322^2 + 0.737^2} \\
&\approx \sqrt{\strut 0 + 2.512 + 5.391 + 0.543} \\
&= \sqrt{\strut 8.446} \\
&\approx 2.906 \\
\end{align}
[/math]
And so the Euclideanizedlogintegerlimit squared complexity of [math]\frac{5}{3}[/math] is indeed slightly less than that of [math]\frac{5}{1}[/math].
Tunings used in
The [math]\text{ElilsC}()[/math] is used for simplicityweight damage in the minimaxElilsS tuning scheme, known elsewhere as "WeilEuclidean".
Logoddlimitsquared
Out of all the other complexity functions under consideration in this article, the logoddlimitsquared complexity [math]\text{lolsC}()[/math] is most similar to the logintegerlimitsquared complexity [math]\text{lilsC}()[/math], and not only in name. The formula for it is exactly the same, with the one key difference being that all factors of 2 are removed from the interval as an initial step. So if both [math]n[/math] and [math]d[/math] already happen to be odd, this gives the same complexity as the logintegerlimitsquared would.
(Switching to nonlog for this paragraph to make a point efficiently that applies whether log or not.) When we ask for the integer limit of, say, [math]\frac{10}{3}[/math], we simply want the bigger of the numerator and denominator, which in this case is [math]10[/math]. But for the odd limit, we'd first find the factors of 2 — there's exactly one of them, in the numerator — remove them, and then ask for the integer limit again. So we'd change [math]\frac{10}{3}[/math] into [math]\frac53[/math], then find the integer limit of that to be 5. Note that sometimes removing the factors of 2 will change which one of the 'ators is greatest; for example, the odd limit of [math]\frac{10}{9}[/math] is 9, owing to the fact that after removing the factors of 2 (again, just one of them, in the numerator) we're left with [math]\frac{5}{9}[/math], so now it's the denominator with the largest integer.
With its quotientbased form, this removing of prime 2's can perhaps most succinctly be achieved using the mathematical function [math]\text{rough}(n, k)[/math], which takes a number [math]n[/math] and makes it [math]k[/math]rough (yes, that's a technical term: rough number) by dividing out every prime factor less than [math]k[/math]. That's why [math]k[/math] here is equal to 3, not 2, because you have to be 3rough in order to contain no 2's:
[math]
\begin{align}
\text{lilsC}(\dfrac{n}{d}) &= \log_2(\max(n, d)) \\
\text{lolsC}(\dfrac{n}{d}) &= \log_2(\max({\color{red}\text{rough}}(n, 3), {\color{red}\text{rough}}(d, 3))) \\
\end{align}
[/math]
When we look at the vectorbased forms, we just need to drop the first entry of the vector, assuming that is the entry which contains the count of prime 2's as is typical. Otherwise you can drop whichever entry contains prime 2, or if none of them do, then perhaps you should reconsider which complexity function you're using. Unfortunately there's not a particularly clean notation for a variation on a vector which has had its first (or some arbitrary) entry dropped. However, we do have a pretty clean way to express this in summation form. You just set the initial summation index to 2 instead of the typical 1, so you skip the 1st entry, 1st prime, etc.:
[math]
\begin{align}
\text{El}{\color{red}\text{i}}\text{lC}(\textbf{i}) &= \sqrt{ \sum\limits_{n={\color{red}1}}^d\log_2{\!p_n} · \mathrm{i}_n^2 + {\large}\sum\limits_{n={\color{red}1}}^d(\log_2{\!p_n} · \mathrm{i}_n){\large}^2 } \\ \small
\text{El}{\color{red}\text{o}}\text{lC}(\textbf{i}) &= \sqrt{ \sum\limits_{n={\color{red}2}}^d\log_2{\!p_n} · \mathrm{i}_n^2 + {\large}\sum\limits_{n={\color{red}2}}^d(\log_2{\!p_n} · \mathrm{i}_n){\large}^2 } \\ \small
\end{align}
[/math]
Because all we've done is deleted an entry from the vector, nothing has changed between [math]\text{lilsC}()[/math] and [math]\text{lolsC}()[/math] that would affect the elaborate process we followed to get it into the form of a norm. So that all works the same way here.
Tunings used in
The logodd limit is also known within the community as the "Kees height". This name comes from the tuning theorist Kees Van Prooijen, though the explanation for the attribution is not known to us. And so one could be forgiven for assuming that the tuning scheme which minimizes damage with weight based on Kees height across all intervals would have been called Kees tuning. Unfortunately, that is not the case!
Minimizing the Keessimplicityweight damage across all intervals turns out to be equivalent to minimizing the Weilsimplicityweight damage across all intervals accompanied by an heldoctave constraint. This equivalence is clearer when we use our systematic naming: minimizing the logoddlimitsimplicityweight damage across all intervals is equivalent to minimizing the logintegerlimitsimplicityweight damage across all intervals with an heldoctave constraint. And so the point is that this Keesdamageminimizing tuning is not named for Kees, but is referred to with respect to the Weil tuning, that is, it is called the constrainedoctave Weil tuning.
But then apparently the community ended up using the name "Kees" anyway, to refer to the destretched version of the logintegerlimitsquared tuning. In other words, the dumb way of achieving unchanged octaves. So it's an absolute worsecase scenario with the historical names, where the Kees tuning doesn't correspond with the Kees complexity, and what it does refer to is something dumb that probably shouldn't be given a second thought anyway.
Better to stick with our systematic naming where minimaxlolsS does what it says it does and minimaxlilsS does what it says it does.
If you want a mnemonic for when you see "Kees" used around town and can never remember what Kees is supposed to be known for, you can remember that "odd" has a double 'd', and "Kees" has a double 'e', so "Kees" is associated with oddlimitsquared complexity. But how much easier it is to remember that "lol" stands, not only for "laugh out loud", but also for "log odd limit".
Euclideanized logoddlimitsquared
By this point, there's little more that needs to be said. This is a Euclideanized version of the logodd limit, so substitute 2's for 1's in the powers and roots. And logoddlimitsquared is like logintegerlimitsquared but with the factors of 2 removed from the interval. So the Euclideanized logoddlimitsquared complexity [math]\text{ElolsC}()[/math] is twice removed from the logintegerlimitsquared complexity [math]\text{lilsC}()[/math].
Tunings used in
The [math]\text{ElolsC}()[/math] is used in minimaxElolsS tuning scheme. As with minimaxlolsS not being equivalent to Kees, this tuning is not equivalent to "KeesEuclidean" (KE).
Alternative norm powers
As we did in the conventions article table, we did not show any tuning schemes with norm power other than 1 or 2, i.e normal (taxicab) schemes or Euclideanized schemes, respectively. If we were to have shown any other norm power, it would be [math]∞[/math], for completeness, given that it is the dual power of [math]1[/math] and that set of three powers are the ones with special qualities that show up again and again. But we chose not to include it because it would take up so much space and for so little value, since no one actually uses tuning schemes that use those types of complexity. If you want to, it's not hard to name. Simply use 'M' for "maxize" in the same way we use 'E' for "Euclideanize". Why an uppercase 'M'? Because it's named after Max the magician, of course.
But some folks may want to use still other norm powers besides the three key powers of [math]∞[/math], [math]2[/math], and [math]1[/math]. To name such tuning schemes where we don't have a letter to alias it, just use the number in the same position. So a tuning midway between minimax(t)S and minimaxES, or in other words minimax1S and minimax2S, would simply be minimax1.5S (hyphenation is up to you, but we feel once you've gone beyond a single character number, adding the hyphen makes it clearer). And this isn't just for allinterval tuning schemes; e.g. we could see something like the TILT miniRMS5copfrC tuning scheme, where the RMS of 5ized copfrcomplexityweight damage to the TILT is minimized..
We note that allinterval tuning schemes involve two norms each: the interval complexity norm that actually weights the absolute error to obtain the damage which we minimize over the intervals, and the retuning magnitude norm which is what we minimize in order to achieve the indirect minimaxing of said damage across all intervals, by leveraging the dual norm inequality. Which of these two norms do we name the tuning scheme after? As can be seen throughout this article, and centralized in the table later #Alternative complexity allinterval tuning scheme table, we choose the interval complexity norm, because the general idea with our tuning scheme systematic names is to describe what type of damage is minimized and how it is minimized, so explaining the complexity or simplicity weight of the damage is the more direct way to answer that, while the dual norm that's minimized on the retuning magnitude is little but an implementation detail that doesn't even necessarily need to be known about in order to understand the gist of the tunings the scheme produces. And for ordinary (non allinterval) tuning schemes there is no dual norm minimized on the retuning magnitude.
Systematizing norms
Perhaps unsurprisingly, all of the complexity functions we investigate in this article for their value in tuning theory are related in a systematic way, and can be understood in terms of four complexity traits:
 operation: is it a sum (arithmetic), or a product (geometric)
 pretransformer: do we pretransform such that we count the primes, take their logs, or take them themselves
 Euclideanize: do we change the norm power from [math]1[/math] to [math]2[/math]
 replacediminuator: do we replace the smaller of the numerator and denominator with the larger of the two, or not
(We could choose to include an additional trait, odd, that would optionally drop all occurrences of prime 2. However, since the results of this complexity effect on tuning is achievable outside of complexity — via a heldoctave constraint — we find it insufficiently valuable to warrant exploding our option set by another twofold here.)
With 2, 3, 2, and 2 possibilities for each of those traits, respectively, that's a total of 2×3×2×2 complexity functions. Except there are a few combinations of these which aren't productive:
 We can't Euclideanize producttype complexities, as they aren't expressible as norms in the first place.
 We can't use counts of primes with producttype complexities either, or at least, that will result in the complexity always coming out to 1 (because 1 times itself n times is still 1)
So, we actually only have 16 total complexities in this family. Of these, we've already discussed 10. And of those 6 remaining, 4 of them are of potential interest for tuning. We'll discuss those soon. But first, here's a table:
  replacediminuator  

sum  prime \ norm power  1  2  prime \ norm power  1  2  
count  copfr  Ecopfr  count  doublecopfrlimit  doubleEcopfrlimit  
log  lp  Elp  log  lils  Elils  
itself  sopfr  Esopfr  itself  doublesopfrlimit  doubleEsopfrlimit  
product  prime \ norm power  1  2  prime \ norm power  1  2  
count      count      
log  prodoflog    log  prodofloglimitsquared    
itself  prod    itself  ils   
^{[12]}
Every one of the 6 members of the topleft box is one of the 10 complexities we've talked about already: copfr, lp, sopfr, and the Euclideanized versions of each. Another 2 can be found in the bottom two boxes, one each: product complexity (prod), and integerlimitsquared complexity (ils). The remaining 2 can be found in the topright box, along its middle row: logintegerlimitsquared and its Euclideanized version.
So of the 6 remaining, the 2 that are likely of no further interest are the other 2 in the lower boxes, i.e. the other two producttype complexities. These cannot be normified, being products, so they are already of no use as allinterval tunings. And the proof that lets us minimize the prod simplicity weight damage via the sopfr simplicity weight damage will not analogously hold for these complexities and their arithmetic equivalents, on account of the fact that some logs of primes are less than 2 (namely 2 and 3) and thus their products are actually less than their sums; so this is to say that we cannot proxy minimize them via their arithmetic analogs. And, they're just sort of strange; the "prodoflog" complexity takes the product of the logs of the primes, such that the complexity of 9/5 would be [math]1.585 × 1.585 × 2.322 \approx 5.833[/math], which feels a bit like an abomination, a thing that should not be.
But let's take a moment to reflect on the other 4 remaining. Essentially what we've done with this "replacediminuator" trait is generalized the "integerlimitsquared effect" whereby only the greater of the numerator and denominator is retained, with the lesser being replaced with a copy of the one retained. But there's no reason we can't apply this idea outside the context of product or logproduct complexity. For example, the doublecopfrlimit complexity of 9/5 would be 4, because we have 2 total primes in the numerator and only 1 total prime in the denominator so the greater of those 2 values, doubled, is our complexity.
Before proceeding, let's review the natures of the relationships between the complexities we already know about, though.
Producttype complexities
Another way to look at the problem with product complexity, i.e how being a product rather than a sum, it has no norm form, is that all of the complexities that are useful for allinterval tunings are arithmetic, insofar as are calculated as sums. It's not just logproduct complexity that is like this. copfr and sopfr are also like this. Sopfr has it in the name: it's a sum of prime factors. We could alternatively call logproduct complexity the "solopfr" for "sum of logs of prime factors with repetition" to make this clearer, and copfr could be rephrased as "sum of 1s per each prime factor with repetition". So our arithmetic functions are cofpr, lp, sopfr, lil, and lol. Our geometric functions are prod, il, and ol.
Note that prod is not actually the direct geometrification of lp; it's actually the direct geometrification of sopfr. The direct geometrification of lp would be the function that found the products of the logs of the primes; the abomination mentioned a moment ago.
And you might think that since the [math]1[/math]norm of a logprimepretransformed vector is the same as [math]\log(n·d)[/math], that the [math]1[/math]norm of a primepretransformed vector would be the same as [math]n·d[/math] — not so; it's obvious what [math]n·d[/math] should be when you think about it in isolation; it's the product of the primes raised to the absolute values of the respective entries. If you just sum the primes' products with the absolute values of the respective entries, you get sopfr.
Comparing sopfrC, prodC, and lpC
[math]\text{sopfrC}()[/math] bears a close similarity to product complexity [math]\text{prodC}()[/math], as we can see through their summation and product forms. [math]\text{prodC}()[/math] is the same thing as [math]\text{sopfrC}()[/math] except that it multiplies rather than exponentiates, and sums rather than multiplies:
[math]
\begin{array} {c}
\text{sopfrC}(\textbf{i}) & = & {\color{red}\sum\limits_{{\color{black}n=1}}^{{\color{black}d}}}(p_n {\color{red}·} \mathrm{i}_n) & = & (p_1 {\color{red}·} \mathrm{i}_1) & {\color{red}+} & (p_2 {\color{red}·} \mathrm{i}_2) & {\color{red}+} & \ldots & {\color{red}+} & (p_d {\color{red}·} \mathrm{i}_d) \\
\text{prodC}(\textbf{i}) & = & {\color{red}\prod\limits_{{\color{black}n=1}}^{{\color{black}d}}}(p_n{\color{red}\text{^}}^{\mathrm{i}_n}) & = & (p_1{\color{red}\text{^}}^{\mathrm{i}_1}) & {\color{red}×} & (p_2{\color{red}\text{^}}^{\mathrm{i}_2}) & {\color{red}×} & \ldots & {\color{red}×} & (p_d{\color{red}\text{^}}^{\mathrm{i}_d}) \\
\end{array}
[/math]
[math]\text{sopfrC}()[/math] also bears a close similarity to log product complexity [math]\text{lpC}()[/math] (which we'll look at through their norm forms here), on account of the fact that the [math]L[/math] in [math]\text{lpC}(\textbf{i}) = ‖L\textbf{i}‖_1[/math] can be replaced with [math]L = \text{diag}(\textbf{ℓ}) = \text{diag}(\log_2{\!𝒑})[/math], it's the same thing except that it pretransforms entries by the primes themselves, rather than their logs:
[math]
\begin{array} {c}
\text{sopfrC}(\textbf{i}) & = & ‖\text{diag}({\color{red}𝒑})\textbf{i}‖_1 & = & ({\color{red}p_1} · \mathrm{i}_1) & + & ({\color{red}p_2} · \mathrm{i}_2) & + & \ldots & + & ({\color{red}p_d} · \mathrm{i}_d) \\
\text{lpC}(\textbf{i}) & = & ‖\text{diag}({\color{red}\log_2{\!𝒑}})\textbf{i}‖_1 & = & ({\color{red}\log_2{\!p_1}} · \mathrm{i}_1) & + & ({\color{red}\log_2{\!p_2}} · \mathrm{i}_2) & + & \ldots & + & ({\color{red}\log_2{\!p_d}} · \mathrm{i}_d) \\
\end{array}
[/math]
Comparing copfr and sopfr
In this way we could imagine [math]\text{sopfr}()[/math] as summing each prime factor raised to the [math]1[/math]^{st} power, while [math]\text{copfr}()[/math] sums each prime factor raised to the [math]0[/math]^{th} power:
[math]
\text{copfr}(20) = \text{copfr}(2·2·5) = 2^{\color{red}0} + 2^{\color{red}0} + 5^{\color{red}0} = 1 + 1 + 1 = 3 \\
\text{sopfr}(20) = \text{sopfr}(2·2·5) = 2^{\color{red}1} + 2^{\color{red}1} + 5^{\color{red}1} = 2 + 2 + 5 = 9 \\
[/math]
Also like [math]\text{sopfrC}()[/math], the [math]\text{copfrC}()[/math] function can be expressed as the [math]\text{copfr}()[/math] of the numerator times the denominator, or as the sum of the [math]\text{copfr}()[/math] of them separately. [math]\text{lpC}()[/math] follows this pattern too, with [math]\log_2()[/math] as its subfunction:
[math]
\begin{array} {c}
\text{copfrC}(\frac{n}{d}) & = & \text{copfr}(nd) & = & \text{copfr}(n) & + & \text{copfr}(d) \\
\text{lpC}(\frac{n}{d}) & = & \log_2(nd) & = & \log_2(n) & + & \log_2(d) \\
\text{sopfrC}(\frac{n}{d}) & = & \text{sopfr}(nd) & = & \text{sopfr}(n) & + & \text{sopfr}(d) \\
\end{array}
[/math]
We placed the functions in that order — with [math]\text{lpC}()[/math] in the middle — as preparation for the following observation, which is that these three functions set up a sort of progression in relative influence of the prime factors, from being flat, to being proportional to their size. That is, we could insert [math]\text{lpc}()[/math] into the middle of the previous example as a sort of midway point between [math]\text{copfrC}()[/math] and [math]\text{sopfrC}()[/math].
[math]
\begin{array} {c}
\text{copfrC}(\frac95) & = & \text{copfr} & ( & 3 & · & 3 & · & 5 & ) & = & 1 & + & 1 & + & 1 & = & 3 \\
\text{lpC}(\frac95) & = & \text{lp} & ( & 3 & · & 3 & · & 5 & ) & \approx & 1.585 & + & 1.585 & + & 2.322 & \approx & 5.491 \\
\text{sopfrC}(\frac95) & = & \text{sopfr} & ( & 3 & · & 3 & · & 5 & ) & = & 3 & + & 3 & + & 5 & = & 11 \\
\end{array}
[/math]
In vectorbased form, [math]\text{copfrC}()[/math] is exactly equivalent to the [math]1[/math]norm of the interval vector [math]\textbf{i}[/math], not pretransformed in any way, which is to say, the sum of the absolute values of each entry:
[math]
\small \begin{align} \text{copfrC}(\textbf{i}) &= ‖\textbf{i}‖_1 \\ \small
&= \sqrt[1]{\strut \sum\limits_{n=1}^d \mathrm{i}_n^1} \\ \small
&= \sqrt[1]{\strut \mathrm{i}_1^1 + \mathrm{i}_2^1 + \ldots + \mathrm{i}_d^1} \\ \small
&= \mathrm{i}_1 + \mathrm{i}_2 + \ldots + \mathrm{i}_d
\end{align}
[/math]
Replacingdiminuator: shear
The "replacingdiminuator" trait can be visualized on a lattice by shearing the lattice (sometimes confusingly called "skewing"^{[13]}). The horizontal coordinate becomes the sum of the original horizontal coordinate plus some fraction of the vertical coordinate (and any other coordinates). This is how it is related to making the size of intervals count toward their complexity: this stretches the lattice along the alldimensional diagonal where in one direction every prime count is increasing and in the other direction every prime count is decreasing, in order to make distances in that direction longest.
Here is a series of diagrams that show how this affects things for primes 3 and 5 via the 12 complexities in the top two boxes of the table above:
If you're struggling to compute the complexities on the right half of the screen, here's an example that might help. The bottom right Edoublesopfrlimit of 5/3. Normally that vector is [0 1 1⟩. But we're doing sofpr here so it's prescaled by the primes themselves, so that's [0 3 5⟩. But we're doing the doublesopfrlimit, which means we only want stuff from the numinator, and to achieve that we augment the vector with the sum of all the entries. Not their absolute values. So that's 0 + 3 + 5 = 2. So the augmented vector is [0 3 5  2⟩. And we take the norm of that, so just sum those absolute values up 0 + 3 + 5 + 2 = 10. But wait, that's actually just the doublesopfrlimit; I said we'd do the Edoublesopfrlimit. Well, same deal but sum squares and root, so 0² + 3² + 5² + 2² = 38, so the result is √38 ≈ 6.164, as you can see in the diagram. Note that for this tuning, 5/3 is considered less complex than 5/1, because the issue of size apparently outweighs the product complexity issue.
It's important to recognize that norms measure from origin to point, not point to point; measurements between arbitrary points are instead handled by something called "metrics" (yes, that's a technical mathematical term). So while one might wish for the underlying lattices in the 3rd column to reflect the fact that 5/3 and 5/1 have the same complexity, and thus occupy the same point in space with 0 distance between them, that's simply not the right way to think about it. One should have no more problem with the fact that the difference in complexity along the edge that connects 5/3 and 5/1 causes a change in complexity of 0 when you're on the logintegerlimitsquared diagram than I should for how in any Euclideanized diagram, let's take the Ecopfr one for the simplest example, one can go diagonally to 15/1 from 1/1 for a complexity of √2 and straight from 1/1 to 5/1 for a complexity of 1, but one doesn't complain that between 5/1 and 15/1 should be a move worth 1 yet considered in and of itself but in this context seems like it should be √2  1. The conclusion is: you don't worry about that, because you never travel along that path when reaching points straight from the origin. It's irrelevant to the definition of the norm.
Hybrids between integerlimitsquared and product complexity
This section develops upon ideas introduced earlier in the Normifying: complexity and size section.
This section is as much about hybrids between logintegerlimitsquared and logproduct complexity as it is about the nonlog versions of its title. In fact the log versions are more useful. But it is simpler to discuss the ideas we want to discuss here without having to mention logs all the time. So that's what we'll do.
Reviewing the problem
The concept of integer limit was introduced by Paul Erlich as a replacement for odd limit when octaves are tempered. But it was Mike Battaglia who realised that integerlimitsquared complexity could be expressed as a norm, and could therefore be used as the basis for an allinterval tuning scheme in the way Paul had used product complexity.^{[14]} Essentially, Paul's "Tenney" tunings use [math]n×d[/math], while Mike's "Weil" tunings use [math]\max(n,d)[/math]. The main idea behind this innovation was that product complexity does not account for interval size. Said another way, product complexity does not account for the balance of integers across the fraction bar. For example, [math]\frac53[/math] has the same product complexity as [math]\frac{15}{1}[/math], since both 5×3 and 15×1 are equal to 15. By comparison, according to integerlimitsquared complexity, [math]\frac{15}{1}[/math] with complexity 15 is considered much more complex than [math]\frac53[/math], which only has a complexity of 5.
But as we noted earlier, integerlimitsquared complexity has its own blind spot. Where product complexity is blind to the balance between the numerator and denominator, integerlimitsquared complexity is blind to the denominator completely (this statement assumes that we deal only with superunison intervals, but that is a safe assumption because all subunison intervals have reciprocal equivalent with a superunison interval and so we only need to deal with one set or the other, and conventionally we deal only with the superunison set^{[15]}). For example, according to integerlimitsquared complexity, both [math]\frac53[/math] and [math]\frac54[/math] have the same complexity of 5, while according to product complexity these are differentiated, with complexities of 15 and 20, respectively.
A complexity statistic which is blind neither to balance nor to the denominator itself is of theoretical interest for use with musical intervals, because both of these aspects contribute to auditory perception of harmonic complexity.
Such a complexity statistic may be constructed as a hybridization of product complexity and integerlimitsquared complexity, with each of the two primitive complexities filling in for the blind spot of the other.
We could present the relationship between these three complexities thusly:
complexity  interval property  

balance  diminuator itself  
product  blind  aware 
integerlimitsquared  aware  blind 
hybrid  aware  aware 
The proposed solution
A complexity statistic like this has already been described, by Mike, at the same time he introduced integer limits to the theory. In fact, any tuning scheme along his "TenneyWeil continuum" — other than the extremes of pure Tenney and pure Weil — uses a complexity statistic that is a hybrid of product and integerlimitsquared complexities in this way. Thus, tunings resulting from these schemes are blind neither to interval size nor to their full rational content.
The primary means by which this is achieved is a tradeoff factor that Mike deems [math]k[/math]^{[16]} which appears in the bottom row of the sizesensitizing matrix [math]Z[/math]. We taught (here: #Normifying: sizesensitizing matrix) that matrix as having a bottom row of all 1's. Technically speaking, though, those are all [math]k[/math], like so:
[math]
\begin{array} {c}
Z \\
\left[ \begin{array} {r}
1 & 0 & 0 \\
0 & 1 & 0 \\
0 & 0 & 1 \\
\hline
\style{backgroundcolor:#FFF200;padding:5px}{k} & \style{backgroundcolor:#FFF200;padding:5px}{k} & \style{backgroundcolor:#FFF200;padding:5px}{k} \\
\end{array} \right]
\end{array}
[/math]
This is because minimaxlilsS is the tuning where this [math]k[/math] value equals 1. And minimaxS is the tuning where it equals 0, and thus we can simply drop the extra bottom row of the matrix, leaving the rest as an identity matrix to vaporize. There's no reason [math]k[/math] can't go higher than 1, but at that point you're exploring tunings where you've theoretically more than ignored the value of the diminuator, so your results will likely become unpredictable or strange. The values of most interest, clearly, are values somewhere between 0 and 1.
So 0 is logproduct complexity and 1 is logintegerlimitsquared. In terms of the size and complexity components, 1 is where they are perfectly balanced, half and half, as is seen in the formula. 0 is where size is not considered. And ∞ is where only size is considered (whatever that means).
While we're on the topic, we may note that when computing tunings with hybrid norms like this, the required augmentation to [math]M\mathrm{T}S_\text{p}[/math] follows a similar change. An example for an allinterval tuning (where [math]\mathrm{T} = \mathrm{T}_{\text{p}} = \mathrm{I}[/math]) is visualized here:
[math]
\left[ \begin{array} {rrrr}
\frac{1}{\log_2{2}} & \frac{2}{\log_2{3}} & \frac{3}{\log_2{5}} & \style{backgroundcolor:#FFF200;padding:5px}{0} \\
0 & \frac{3}{\log_2{3}} & \frac{5}{\log_2{5}} & \style{backgroundcolor:#FFF200;padding:5px}{0} \\
\hline
\style{backgroundcolor:#FFF200;padding:5px}{k} & \style{backgroundcolor:#FFF200;padding:5px}{k} & \style{backgroundcolor:#FFF200;padding:5px}{k} & \style{backgroundcolor:#FFF200;padding:5px}{1} \\
\end{array} \right]
[/math]
In our systematic nomenclature, Tenney tuning is minimaxS and Weil tuning is minimaxlilsS. We will be continuing onward using those preferred descriptive names. And as for hybrids, we suggest naming the complexity [math]k\text{iphC}()[/math] where "iph" stands for "integerlimitsquared/product hybrid. So a corresponding tuning could be the TILT minimax½iphC.
And for the logarithmic variants, we suggest [math]k\text{liphC}()[/math] where "liph" stands for "log integerlimitsquared/product hybrid. This version is normifiable, and could be used for allinterval tunings, such as minimax½liphS.
Here's a continuum of (allinterval) minimaxkliphS tunings of meantone:
 [math]k=0[/math]: ⟨1201.699 1899.263 2790.258] (minimaxS)
 [math]k=\frac14[/math]: ⟨1201.273 1898.591 2789.271] (minimax¼liphS)
 [math]k=\frac12[/math]: ⟨1200.849 1897.920 2788.284] (minimax½liphS)
 [math]k=1[/math] ⟨1200.000 1896.578 2786.314] (minimaxlilsS)
 [math]k=2[/math] ⟨1198.306 1893.902 2782.381] (minimax2liphS)
And here is a diagram showing constant damage contours for these [math]k[/math]values:
Limitations of hybridization
We must be clear that while functions along the iph and liph complexity continua do evaluate intervals based on a combination of their size and (a particular notion of) their complexity, these functions are not suitable substitutes or replacements for a dedicated size limit. They do not combine size and complexity in a way that is as meaningful as it may seem at first.
At least, in the context of tuning problems such as we face in RTT — systematic selection of intervals to use as tuning targets, and then among the ones which do pass the filter to be considered as targets at all, how to distribute priority amongst them — one may not cite hybrid size/complexity functions like these as a comparable solution to independently setting a size limit and a complexity limit (see: Dave Keenan & Douglas Blumeyer's guide to RTT: tuning fundamentals#Truncated integer limit triangle (TILT) for more information on this sort of programmatic targetinterval set selection). Perhaps the most succinct way to explain why would be: because these hybrids are literally averages of their size and complexity components, and so could never on their own fulfill a role that is more like the intersection of a size limit and a complexity limit. Or said another way: ultimately it is still just a single complexity measurement, though it may be sensitive to size where other complexity functions aren't at all.
What might be fair to say is that an integer limit and a complexity limit fulfill similar roles in the selection and weighting of a targetinterval set, but neither of them have anything like the same effect as a true size limit does, as can be seen clearly in this diagram, where we can see that hybrids of integerlimitsquared complexity and logproduct complexity will vary between the shapes of the green and blue, but will never look anything like radiating red lines from the origin.
An illuminating way we found to frame this limitation is: sure, it sounds great to say that an integer limit combines a size limit and complexity limit, but it could just as easily be said the converse, that a complexity limit combines a size limit and an integer limit! At a certain point, there just isn't as meaningful of a difference between integer and complexity limits as there is between either of these two things and a size limit. An integer limit is really just a special [math]k[/math]value of a complexity limit, where the contour line ends up going straight vertical on this diagram.
So perhaps the greatest thing we could hope to get out of a hybrid like this would be to consolidate our integer and complexity limits down into a single hybridintegercomplexity limit. But you would always still need a size limit, too.
At least, this is what we might say when speaking of ordinary tunings that use targetinterval set schemes such as TILT which have the possibility of enforcing a size limit. If your thinking is limited to allinterval tuning schemes, then something like one of these hybrid functions may be your best bet.
Value for allinterval tunings vs ordinary tunings
The minimaxlilsS tuning scheme — the one Mike introduced integer limits to our theory in the service of — is an allinterval tuning scheme. Tuning schemes such as these (as discussed in more detail on their dedicated article) are not best suited for finding tunings good in actual musical performance; rather they are favored for their computational expediency as well as the benefit of not having to specify a targetintervals set to tune with respect to (which can be a fuzzy and contentious process), both of which lead to this category of tuning schemes being preferred when documenting temperaments, especially in computer automated situations. This is all to say that the actual tuning schemes in the minimaxkliphS (WeilTenney) continuum are of dubious value themselves, because their complicated hybrid nature, as well as the fuzzy and contentious choice of exact position along the continuum, should counteract the primary motivations for using allinterval tuning schemes in the first place. The whole raison d'être of these schemes is their mathematical simpleness, so if you take that away, what's the point? It would be better to stick with simply minimaxS, or perhaps even minimaxlilsS, then.
However, the hybrid complexity statistics that Mike developed for them are of potentially massive value in nonallinterval tuning schemes, that is to say the ordinary kind for which the specification of a targetintervals set to tune with respect to is required. For these tuning schemes, computational complexity is not at as much of a premium as it is with allinterval tuning schemes, and also there's no check on the arbitrary interests of the particular human doing the tuning; what's more important here is to nail as best as possible the distribution of damage across the intervals whose damage is being minimized. So hybrids of product and integerlimitsquared complexity may be of interest here. Theoretically, tunings in the middle of the liph continuum could be superior to either logproduct or logintegerlimitsquared on the extremes. But the tradeoff versus complexity is not clear enough at this time that we would dislodge logproduct from our position of default complexity.
In the development of the N2D3P9 statistic, we found an approximate 3:2 ratio in how the size of a ratio's numerator and denominator affected its popularity in musical scales. This fact cannot be directly applied to RTT tuning, as in N2D3P9's case we're actually dealing with interval classes that are not only 2free (octavereduced) but also 3free (essentially fifthreduced), and popularity in scales is only handwavily akin to intervallic complexity, but we cite it here anecdotally, as a speculation on how various balances of product and integerlimitsquared complexity may come to be preferred.
A defense of our choice of logproduct as default complexity
Probably the most important factor for us in considering a complexity function to be high quality is this: is it monotonic over the integers, and hence is psychoacoustically plausible. For anything to be rightfully called damage, we believe the weighting of absolute error must have psychoacoustic plausibility; otherwise, while it may be of interest otherwise, it is not what we are looking for in this application: a way of optimizing approximations of harmonic music for humans to listen to.
Several of the complexity functions we look at in this article have this property. Product complexity and integerlimitsquared complexity both do, as do all their hybrids, and their logarithmic variants. Any Euclideanized complexity will lose this effect. And copfr and sopfr do not have it.
Between these "integermonotonic complexities", however, there may not be a strong case for any one of them having any more specific audible plausibility than another. This question probably can't be answered legitimately with a computer algorithm or clever math trick; it would probably need to be answered with experimentation on actual human subjects.
And so, barring that level of realworld effort, for now, we find it's a reasonable position to choose among these integermonotonic complexities the one which has several pluses:
 relatively long history of use in tuning
 relative ease of understanding and computation
 that special proportional effect to size in cents (discussed here
Computing ordinary tunings using these complexities
For ordinary tunings with finite targetinterval sets, we already have everything you need to tune with respect to any complexity you might dream of. In the computations article we relied only on a single complexity function, our default of logproduct complexity. This meant that whenever it came time to prepare our weight matrix [math]W[/math], we simply found [math]\log_2{\!nd}[/math] for each of our targetintervals and put them in a diagonal matrix:
[math]
\begin{align}
\text{if} \; \mathrm{T} = [ \frac21, \frac32, \frac32, \frac43, \frac52, \frac53, \frac54, \frac65 ],
\\
W &=
\left[ \begin{array} {rrr}
\log_2{2·1} & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & \log_2{3·1} & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & \log_2{3·2} & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & \log_2{4·3} & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & \log_2{5·2} & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & \log_2{5·3} & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & \log_2{5·4} & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & \log_2{6·5} \\
\end{array} \right]
\\
&=
\left[ \begin{array} {rrr}
1.000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 1.585 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 2.585 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 3.585 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 3.322 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 3.907 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 4.322 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 4.907 \\
\end{array} \right]
\end{align}
[/math]
We could reciprocate the whole thing to find a simplicityweight matrix [math]S[/math] and if we leave this as is then it's our complexityweight matrix [math]C[/math].
But the point is here that there's nothing stopping us from using whatever bizarre complexity function we might dream up, such as, let's say, the "sum" complexity, which by analogy with product complexity, takes the sum of the numerator and denominator rather than their product. In that case, we'd find:
[math]
C =
\left[ \begin{array} {rrr}
2+1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 3+1 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 3+2 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 4+3 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 5+2 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 5+3 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 5+4 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 6+5 \\
\end{array} \right]
=
\left[ \begin{array} {rrr}
3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 4 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 5 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 7 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 7 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 8 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 9 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 11 \\
\end{array} \right]
[/math]
For ordinary tunings, there are no constraints on your complexity function at all, other than it return real numbers, perhaps. Go as crazy as you like. There's no need for it to be in norm form so that its dual norm can be figured out so that you can use it in a dual norm inequality or anything like that. Anything goes. Have fun. Or maybe just stick with logproduct complexity.
Computing allinterval tuning schemes with alternative complexities
As opposed to ordinary tunings, which were briefly covered in the previous section, for allinterval tunings, we do have restrictions: specifically, we needed to get our complexity into the form of a pretransformed norm, for which we could then find its dual, as the norm with the dual power and inverse pretransformer.
As covered in our allinterval tuning schemes article Dave Keenan & Douglas Blumeyer's guide to RTT: allinterval tuning schemes#Formula relating dual powers, the dual power is found by [math]\frac{1}{p} + \frac{1}{\text{dual}(p)} = 1[/math]. And typically the inverse pretransformer is found by the matrix inverse (but this is not always so simple).
The common setup for every allinterval tuning scheme is that we're going to find our generator tuning map [math]𝒈[/math] for a given temperament's mapping matrix [math]M[/math], and we're going to do it by minimizing the greater side of the dual norm inequality, [math]‖𝒓X^{1}‖_q[/math]. What makes the difference from one tuning scheme to the next, with their different alternative complexities, is what we use for [math]X^{1}[/math], the inverse pretransformer matrix which is the inverse of the complexity pretransformer matrix we've chosen; this is the one that we've been using on intervals when computing their complexities using the norm form of their formulas. And also the choice of optimization power [math]p[/math], which for these examples will always either be [math]∞[/math] or [math]2[/math] (2 for the Euclideanized ones).
To unpack the [math]𝒈[/math] and [math]M[/math], we can expand the expression we're minimizing like so:
[math]
‖𝒓X^{1}‖_q \\
‖(𝒕  𝒋)X^{1}‖_q \\
‖(𝒈M  𝒋)X^{1}‖_q
[/math]
For the following set of examples, we'll be using porcupine temperament, with [math]M[/math] = [⟨1 2 3] ⟨0 3 5]}. Thus we find [math]𝒕 = 𝒈M = [/math] ⟨[math]g_1[/math] [math]2g_1+3g_2[/math] [math]3g_1+5g_2[/math]]. And being in the 5limit, we find [math]𝒋[/math] = ⟨1200 1901.955 2786.314]. So:
[math]
\min(
‖
(
\begin{array}
𝒕
\left[ \begin{array} {l}
g_1 & & 2g_13g_2 & & 3g_15g_2
\end{array} \right]
\end{array}

\begin{array}
𝒋
\left[ \begin{array} {l}
1200 & & 1901.955 & & 2786.314
\end{array} \right]
\end{array}
)X^{1}
‖_q
)
[/math]
Simplifying down to one map there in the middle, instead of subtraction (i.e. going back from [math]𝒕  𝒋[/math] to [math]𝒓[/math]):
[math]
\min(
‖
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
X^{1}
‖_q
)
[/math]
And then recognizing that we actually substitute a simpler and in this case equivalent power mean for the power norm when computing these:
[math]
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
X^{1}
\largeRRzigzag _p
)
[/math]
So that's going to be our starting point for all of the following computations.
Logproduct
In the cases of the minimaxS and minimaxES tuning schemes, which use logproduct complexity, our complexity pretransformer is the log prime matrix [math]L[/math] and thus our inverse pretransformer, is [math]L^{1}[/math]. So we substitute that in for [math]X^{1}[/math]:
[math]
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
{\color{red}(L^{1})}
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
\left[ \begin{array} {l}
\frac{1}{\log_2{2}} & 0 & 0 \\
0 & \frac{1}{\log_2{3}} & 0 \\
0 & 0 & \frac{1}{\log_2{5}} \\
\end{array} \right]
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\left[ \begin{array} {l}
\frac{g_1  1200}{\log_2{2}} & & \frac{2g_13g_21901.955}{\log_2{3}} & & \frac{3g_15g_22786.314}{\log_2{5}}
\end{array} \right]
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\left[ \begin{array} {l}
\frac{g_1}{\log_2{2}}  1200 & & \frac{2g_13g_2}{\log_2{3}}  1200 & & \frac{3g_15g_2}{\log_2{5}}  1200
\end{array} \right]
\largeRRzigzag _p
)
[/math]
Here we can see the [math]L[/math]canceling proportionality as we talked about briefly earlier: #Proportionality to size.
To see these tunings worked out with exact solutions in the form of generator embeddings, see Generator embedding optimization#MinimaxS and Generator embedding optimization#MinimaxES.
Sumofprimefactorswithrepetition
In the cases of the minimaxsopfrS and minimaxEsopfrS tuning schemes, which use logproduct complexity, our complexity pretransformer is a diagonalized list of the primes [math]\text{diag}(𝒑)[/math] and thus our inverse pretransformer, is [math]\text{diag}(𝒑)^{1}[/math]. So we substitute that in for [math]X^{1}[/math]:
[math]
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
{\color{red}(\text{diag}(𝒑)^{1})}
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
\left[ \begin{array} {l}
\frac12 & 0 & 0 \\
0 & \frac13 & 0 \\
0 & 0 & \frac15 \\
\end{array} \right]
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\left[ \begin{array} {l}
\frac{g_1  1200}{2} & & \frac{2g_13g_21901.955}{3} & & \frac{3g_15g_22786.314}{5}
\end{array} \right]
\largeRRzigzag _p
)
[/math]
Since we're prescaling by the primes themselves, not their logs as we did with minimax(E)S, we didn't do the final step where we show how each term has a 1200 subtracted from it, in other words, because the scalings aren't proportional to the pitch sizes of the primes like we're using here, but instead their frequencies.
To see these tunings worked out with exact solutions in the form of generator embeddings, see Generator embedding optimization#MinimaxsopfrS and Generator embedding optimization#MinimaxEsopfrS.
Countofprimefactorswithrepetition
In the cases of the minimaxcopfrS and minimaxEcopfrS tuning schemes, which use copfr complexity, our complexity pretransformer is an identity matrix [math]I[/math] and thus our inverse pretransformer, is also an identity matrix [math]I[/math]. In other words, we have no pretransformer here! So we can just delete occurrences of [math]X^{1}[/math]:
[math]
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
{\color{red}(I)}
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
\largeRRzigzag _p
)
[/math]
We should already be able to see that this computation is going to be easier than ever. To see these tunings worked out with exact solutions in the form of generator embeddings, see Generator embedding optimization#MinimaxcopfrS and Generator embedding optimization#MinimaxEcopfrS.
Logintegerlimitsquared
Here's where things start to get pretty weird.
In the cases of the minimaxlilsS and minimaxElilsS tuning schemes, our complexity pretransformer is the sizesensitizing matrix composed with the log prime matrix, [math]ZL[/math] and thus its inverse is [math](ZL)^{1}[/math]. Unfortunately, though, even though it looks like we should be able to say the dual norm [math]\text{lilsC}^{*}()[/math] should be [math]‖𝒓(ZL)^{1}‖_∞[/math], it's not that simple, that is, it's not as simple as the typical case where we can just take the dual power and the inverse pretransformer. For starters, [math]ZL[/math] is rectangular and so it could only have a pseudoinverse, [math](ZL)^{+}[/math], not a true inverse; this might not have been an insurmountable problem, except that for whatever reason (reasons beyond these authors, anyway) this doesn't actually work.^{[17]} Mike Battaglia has worked out a proof that it's this:
[math]
\begin{align}
\text{lilsC}^{*}(\textbf{𝒓}) &= \max( \frac{r_1}{\log_2{\!p_1}}, \frac{r_2}{\log_2{\!p_2}}, \ldots, \frac{r_d}{\log_2{\!p_d}}, 0) \;  \\
& \quad\quad\quad \min(\frac{r_1}{\log_2{\!p_1}}, \frac{r_2}{\log_2{\!p_2}}, \ldots, \frac{r_d}{\log_2{\!p_d}}, 0)
\end{align}
[/math]
And in any case, the way we achieve the dual norm is through customapplied augmentations to the matrices that are used in computing optimized tunings with methods that give exact solutions. So the theory has not quite yet arrived at a truly generalizable approach to dualnormification (though we'd be delighted to be corrected on this matter, if someone has indeed cracked this).
Here's what [math](ZL)^{+}[/math] looks like for our porcupine example:
[math]
\dfrac12
\left[ \begin{array} {rrrr}
\log_2{2} & {\log_2{2}} & {\log_2{2}} & \log_2{2} \\
{\log_2{3}} & \log_2{3} & {\log_2{3}} & \log_2{3} \\
{\log_2{5}} & {\log_2{5}} & \log_2{5} & \log_2{5} \\
\end{array} \right]
[/math]
Which is maybe better understood factored into [math]L^{1}Z^{+}[/math], which it is equivalent to:
[math]
\begin{array} {c}
L^{1} \\
\left[ \begin{array} {c}
\frac{1}{\log_2{2}} & 0 & 0 \\
0 & \frac{1}{\log_2{3}} & 0 \\
0 & 0 & \frac{1}{\log_2{5}} \\
\end{array} \right]
\end{array}
\begin{array} {c}
Z^{+} \\
\dfrac12
\left[ \begin{array} {cccc}
1 & 1 & 1 & 1 \\
1 & 1 & 1 & 1 \\
1 & 1 & 1 & 1 \\
\end{array} \right]
\end{array}
[/math]
When we substitute [math](ZL)^{+}[/math] in for [math]X^{1}[/math]:
[math]
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
{\color{red}((ZL)^{+})}
\largeRRzigzag _p
)
= \\
\min(
\largeLLzigzag
\begin{array} {c}
𝒓 \\
\left[ \begin{array} {l}
g_1  1200 & & 2g_13g_21901.955 & & 3g_15g_22786.314
\end{array} \right]
\end{array}
\begin{array} {c}
(ZL)^{+} \\
\dfrac12
\left[ \begin{array} {rrrr}
\log_2{2} & {\log_2{2}} & {\log_2{2}} & \log_2{2} \\
{\log_2{3}} & \log_2{3} & {\log_2{3}} & \log_2{3} \\
{\log_2{5}} & {\log_2{5}} & \log_2{5} & \log_2{5} \\
\end{array} \right]
\end{array}
\largeRRzigzag _p
)
[/math]
But we won't bother continuing with this approach because whether we use the generic method (such as Wolfram Language's NMinimize[]
) which gives approximate solutions, or the coincidingdamage method which gives exact solutions, we won't get a correct answer. In order to achieve this, we must use the pattern of matrix augmentation that Mike Battaglia has described.
To see these tunings worked out with exact solutions in the form of generator embeddings, see Generator embedding optimization#MinimaxlilsS and Generator embedding optimization#MinimaxElilsS.
Logoddlimitsquared
As mentioned above, the logodd limit based tunings can be computed using the same approach as the logintegerlimitsquared tunings, except with a heldoctave constraint applied. There are apparently other methods of achieving these same result by taking entries corresponding to prime 2 and setting them to zero, in various objects, but we felt that this approach was simpler in the key way insofar as it merely composes computational tricks we already know how to do. But since both the coincidingdamage method and the pseudoinverse method handle constrained optimizations using augmentations already, we end up with doublyaugmented situations, which are fairly gnarly in their own way. But let's work it all out.
To see these tunings worked out with exact solutions in the form of generator embeddings, following the custom technique that Mike worked out, see Generator embedding optimization#MinimaxlolsS and Generator embedding optimization#MinimaxElolsS.
Alternative complexity allinterval tuning scheme table
We'll conclude things here with another monster table. Please compare this with the table of tuning schemes found in the advanced section of our conventions appendix: Advanced: Tuning schemes (you'll need to click the "[Expand]" link there. Everything that can be found in the table below is collapsed into just the top 4 data rows of the table in that conventions article, and that's because the conventions article table does not explode out all of the different alternative complexities like we do here.^{[18]}:
pure
octaves 
retuning magnitude  damage  target
intervals 
systematic name  historical name  

weighting  optimization  
interval complexity  slope  name  power  
dual norm pretransformer  dual norm power  norm pretransformer  norm power  name  multiplier  
held  destretched  name  multiplier  name  power  name  multiplier  name  power  abbreviated  read ("____ tuning scheme")  
inverse logprime matrix  [math]L^{1}[/math]  maximum  ∞  logprime matrix  [math]L = \text{diag}(\log_2{𝒑})[/math]  taxicab  1  simplicityweight (S)  1/complexity  minimax  ∞  all  minimaxS  minimax simplicityweight damage  "TOP"/"TIPTOP"*  
yes  heldoctave minimaxS  heldoctave minimax simplicityweight damage  "CTOP"  
yes  destretchedoctave minimaxS  destretchedoctave minimax simplicityweight damage  "POTOP"/"POTT"*  
(identity matrix)  [math]I[/math]  (identity matrix)  [math]I[/math]  minimaxcopfrS  minimax countofprimefactorswithrepetitionsimplicityweight damage  
inverse diagonal matrix of primes  [math]\text{diag}(𝒑)^{1}[/math]  diagonal matrix of primes  [math]\text{diag}(𝒑)[/math]  minimaxsopfrS  minimax sumofprimefactorswithrepetitionsimplicityweight damage  "BOP"  
(custom handling required, involving augmented matrices)  sizesensitizing matrix, and logprime matrix  [math]ZL[/math]  minimaxlilsS  minimax logintegerlimitsquaredsimplicityweight damage  "Weil"  
yes  destretchedoctave minimaxlilsS  destretchedoctave minimax logintegerlimitsquaredsimplicityweight damage  "Kees"  
yes  heldoctave minimaxlilsS  heldoctave minimax logintegerlimitsquaredsimplicityweight damage  "constrainedoctave Weil"  
minimaxlolsS  minimax logoddlimitsquaredsimplicityweight damage  
inverse logprime matrix  [math]L^{1}[/math]  Euclidean  2  logprime matrix  [math]L = \text{diag}(\log_2{𝒑})[/math]  Euclidean (E)  2  minimaxES  minimax Euclideanizedsimplicityweight damage  "TE"/"TOPRMS"  
yes  heldoctave minimaxES  heldoctave minimax Euclideanizedsimplicityweight damage  "constrainedoctave TenneyEuclidean (CTE)"  
yes  destretchedoctave minimaxES  destretchedoctave minimax Euclideanizedsimplicityweight damage  "pureoctave TenneyEuclidean (POTE)"  
(identity matrix)  [math]I[/math]  (identity matrix)  [math]I[/math]  minimaxEcopfrS  minimax Euclideanizedcountofprimefactorswithrepetitionsimplicityweight damage  "Frobenius"  
inverse diagonal matrix of primes  [math]\text{diag}(𝒑)^{1}[/math]  diagonal matrix of primes  [math]\text{diag}(𝒑)[/math]  minimaxEsopfrS  minimax Euclideanizedsumofprimefactorswithrepetitionsimplicityweight damage  "BE"  
(custom handling required, involving augmented matrices)  sizesensitizing matrix, and logprime matrix  [math]ZL[/math]  minimaxElilsS  minimax Euclideanizedlogintegerlimitsquaredsimplicityweight damage  "WE"  
yes  destretchedoctave minimaxElilsS  destretchedoctave minimax Euclideanizedlogintegerlimitsquaredsimplicityweight damage  "KE"  
yes  heldoctave minimaxElilsS  heldoctave minimax Euclideanizedlogintegerlimitsquaredsimplicityweight damage  "constrainedoctave WeilEuclidean"  
minimaxElolsS  minimax Euclideanizedlogoddlimitsquaredsimplicityweight damage 
We've included some data rows for popular heldoctave and destretchedoctave tuning schemes.
Alternative optimization powers
This topic isn't exactly an "alternative complexity", but it is closely tangential to the topics discussed here, so we felt it was fitting to keep it close by.
The computations article tells us that the general method can handle optimization powers like [math]3[/math], [math]1.4[/math], or whatever, with a method that gives approximate solutions.
Why would someone use a different optimization power? Well, there's no optimization powers possible that are greater than [math]∞[/math] or less than [math]1[/math]. And the tuning qualities imbued by the optimization power change gradually between [math]∞[/math] and [math]1[/math], with [math]2[/math] representing the exact midpoint inbetween them. So if you understand the qualities at those extremes of [math]∞[/math] and [math]1[/math], and that the midpoint of this continuum is [math]2[/math], then you should be able to have a sense about the quality any given optimization power would imbue. We covered this a bit in the fundamentals article already, but as a reminder, [math]p = ∞[/math] is like the "weakest link" approach, where a tuning is only as good as its worsttuned target. On the other extreme, [math]p = 1[/math] is the straightforward "all damage counts" approach, meaning if you add a bit more damage to your besttuned interval, that makes the tuning worse; damage is damage. For a more thorough refresher, you can review Dave Keenan & Douglas Blumeyer's guide to RTT: tuning fundamentals#Rationales for choosing your interpretation. So, if you decide that neither of those extremes work for you, but that you nonetheless lean more one way or the other and don't want to simply accept the midway power of [math]2[/math], then you may be one of the weirdos who wants an optimization power [math]∞ \gt p \gt 2[/math] or [math]2 \gt p \gt 1[/math].
If you do want to do such a thing, how to refer to the tuning scheme? We remind you that when [math]p=∞[/math] we name it "minimax", when [math]p=2[/math] we name it "miniRMS", and when [math]p=1[/math] we name it "miniaverage". But we also remind you that technically speaking these are all minimization of power means, just means of different powers (i.e. optimization powers). So essentially we just unalias these [math]p[/math]means in the name. So a tuning scheme that minimized the [math]2[/math]mean of damage to the TILT would be the "TILT mini2mean" tuning scheme or for short "TILT miniRMS", while [math]3[/math]mean of damage to the TILT would be "TILT mini3mean", which has no shorter form.
We remind the reader that optimization powers are different than norm powers, such as appear in allinterval tuning schemes. For example, the minimaxES tuning scheme uses a Euclideanized complexity, meaning its norm power [math]q[/math] is 2. But it's still a minimax tuning, because it only uses this pretransformed [math]2[/math]norm in the calculations of the complexities, the inverse of which it pretransforms the prime errors by when minimizing their norm; ultimately this dual norm inequality trick is always in service of minimizing the maximum damage across all intervals, i.e. minimaxing that damage. Thus the optimization power of an allinterval tuning scheme is always [math]p = ∞[/math]. As with optimization powers, the powers [math]∞[/math], [math]2[/math], and [math]1[/math] have special qualities. We already explained briefly above how to handle naming with respect to these and other norm powers: Alternative norm powers
So, it is possible to devise a tuning scheme with both the optimization power [math]p[/math] and the norm power [math]q[/math] as something other than one of those three key powers. For example, we could have the TILT mini3mean1.5C tuning. This minimizes the 1.5izedlogproductcomplexityweight damage to the TILT, where the minimization is minimizing the [math]3[/math]mean of the targetinterval damage list. (1.5izing, like Euclideanizing AKA 2izing, would mean to take the powers and roots of 1 in the norm form of the logproduct complexity and swap them out for 1.5's.)
See also
You've almost made it to the end of our article series. Kudos for sticking it out. One article remains, if you are thus inclined to read it too: Tuning in nonstandard domains.
Footnotes and references
 ↑ http://x31eq.com/primerr.pdf, page 4.
 ↑ Previously we entertained the idea that the [math]L\textbf{i}[/math] object inside the norm bars of the logproduct interval complexity could be called an "octavector", on the interpretation that this was the identical [math]L[/math] to the other, i.e. that it had units of oct/p rather than (C). Now we believe those are only complexityannotated primes inside the bars, and upon evaluating the norm, all that remains are annotations or nonvectorized units (p is a vectorized unit). And then you augment the annotation per the norm power. But more on that in the units analysis article.
 ↑ http://x31eq.com/primerr.pdf, starting with last paragraph on page 4
 ↑ "Qoppa" is a term we (D&D) coined for a small quotient between two numbers, analogous to, but one level up the operator hierarchy from a "delta" being a small difference. Like uppercase delta Δ, uppercase qoppa Ϙ is a Greek letter, albeit an archaic one. It can be typed as ⎄QQ using our WinCompose sequences.
 ↑ Actually, [math]\text{sopfr}()[/math] is not typically defined for rationals, only for integers, leaving the question open as to whether factors in the denominator should count positive or negative (i.e. is the [math]\text{sopfr}()[/math] of [math]\frac{7}{5} = 7 + 5 = 12[/math], or does it [math]= 7  5 = 2[/math]?).
 ↑ Missing link, but this was stated somewhere on the Yahoo archives.
 ↑ Including trivially [math]\frac11[/math], where [math]\text{prodC}(\textbf{i}) = 1[/math] and [math]\text{sopfrC}(\textbf{i}) = 0[/math].
 ↑ We have noted with some amusement that "Euclideanized" in many cases could be replaced with the fiveletter initialism "rosos", standing for "root of sum of squares". So leveraging this, we'd find Euclideanized copfr to be "rososcopfr" (ROFLcopter!)
 ↑ Well, it's not a complete mystery. According to Mike Battaglia, he and Keenan Pepper collaborated together to figure out most of the ideas we've explained in this section, with input also potentially coming from Ryan Avela and Gene Ward Smith.
 ↑ The same group which presented itself as a single mathematician by the pseudonym of Nicolas Bourbaki, who brought us the term saturation (see: Saturation,_torsion,_and_contorsion#Saturation and Defactoring_terminology_proposal#Defactoring, to replace saturation) and the use of the symbol "∧" for the exterior product (see: Talk:Meet_and_join#Suggestion_to_change_symbols), but who knows, "he" may have been a force for good in other areas.
 ↑ https://yahootuninggroupsultimatebackup.github.io/tuningmath/topicId_14472#14513, and I suppose we can be grateful that we didn't end up with "Diophantine tuning", then. We almost had "Farey tuning", also, per: https://yahootuninggroupsultimatebackup.github.io/tuningmath/topicId_20956#20966
 ↑ Yes, we did consider whether systemizing the names of these complexities would be an improvement to our systematic tuning scheme naming. However, unlike other components of our tuning scheme naming, such as targetinterval set schemes, damage weight slope, etc. there actually are many established mathematical conventions for these complexities, and we believe we should defer to those when possible. Overconsistentiation is definitely a thing that can happen!
 ↑ The word "skew" means something very different from "shear", no matter whether we are talking geometry or linear algebra. And in everyday language, "skew" tends to mean "rotate". That's not what we are doing here. "Shear" is the word we want.
 ↑ At least, Mike advocated for integerlimit complexity; it's we who are pushing for squaring it, for better parity with product complexity, and to simplify our presentation of Mike's hybrids.
 ↑ Alternatively, we can think of our ratios as consisting of a "numinator" and "diminuator", where the numinator is always the greater of the two and the diminuator is always the lesser of the two.
 ↑ This hybrid tradeoff factor [math]k[/math] is very different from our [math]k[/math] for targetinterval count, which isn't used for allinterval tuning schemes.
 ↑ At least, the MoorePenrose inverse which is the definition of pseudoinverse that we have been assuming throughout this article series thus far does not reliably work for this purpose. That said, if [math]X[/math] is a rectangular complexity pretransformer such as the one used for [math]\text{lilsC}()[/math], there are alternative inverses available which will still effectively leverage the dual norm inequality in order to cap the maximum damage across all intervals. Specifically, any matrix [math]X^{}[/math] (that's [math]X[/math] but with a superscript minus, as opposed to the superscript plus that we use for the standard MoorePenrose inverse) which satisfies [math]X^{}X=I[/math] will suffice. These alternatives follow a parameterized pattern (which you can learn about here: https://mathworld.wolfram.com/Matrix1Inverse.html) which can itself be the subject of an optimization procedure, with the optimization of the generators treated as a nested optimization problem, which understandably is staggeringly computationally intensive. It would be much better if we had a simple closedform expression for the parameters on this pseudoinverse. Since completing this article, we have discovered a truly remarkable proof of such an expression, which this footnote is too small to contain. ;) You can however find it as a workinprogress here: Lils using left inverse.
 ↑ A pedantic note: this table implies a parallelism between "multipliers" and "powers", but truly any multiplier's analog would be an exponent, not a power. A power is the (result of the) entire operation: a base raised to an exponent. For example, 3 to the 2nd power is 9, so 9 is a power (of 3), whereas 2 here is not the power but rather the exponent. However, this pedantic distinction is not important enough here to warrant complicating matters, since in this application we work with power sums, norms, means, and minima, and "power" is a perfectly appropriate word to use in those contexts.