Optimization
In regular temperament theory, optimization is the theory and practice to find low-error tunings of regular temperaments.
A regular temperament is defined by a mapping or a comma basis. It does not contain specific tuning information. To tune a temperament, one must define a tuning map by specifying the size of each generator. The question is what it should be. In general, a temperament is an approximation to just intonation (JI). Any tuning will unavoidably introduce errors on some intervals for sure. The art of tempering seems to be about compromises – to find a sweet spot where the concerning intervals have the least overall error, so that the harmonic qualities of JI are best preserved.
Taxonomy
Roughly speaking, there are two types of tunings with diverging philosophies: prime-based tunings and target tunings.
- A prime-based tuning is optimized for the formal primes, but they are representative for the set of all intervals. There are two perspectives. First, in the tuning space, it minimizes the errors of formal primes. Second, in the interval space, it rates all intervals through a norm, which serves as a complexity measure, and it minimizes the maximum damage (i.e. error divided by complexity) for all intervals.
- A target tuning is only optimized for a particular set of intervals and considers the rest irrelevant and/or infinitely complex.
This article focuses on prime-based tunings. See the dedicated page (→ Target tunings) for target tunings.
Norm
In order to perform prime-based optimization, all intervals must be rated by complexity, so it is critical to employ a norm on the space. There are a few aspects to consider. The weight, which determines how important each formal prime is, and the skew, which determines how divisive ratios are more important than multiplicative ratios. They can be interpreted as transformations of either the space or the norm. The two views are equivalent. In addition, there is the order (or sometimes just dubbed the norm), which determines how the space can be traversed.
Weight
The weight determines the importance of each formal prime. For instance, the Tenney weighter
[math]\displaystyle W = \operatorname {diag} (1/\log_2 (Q)) [/math]
indicates that the prime harmonic q in Q = ⟨2 3 5 …] has the importance of 1/log_{2}(q). Since the tuning space and the interval space are dual to each other, such a rating of importance in the tuning space has the dual effect in the interval space: the prime harmonic q has the complexity log_{2}(q). The more complex it is, the more error will be allowed for it.
Skew
An orthogonal space treats divisive ratios as equally important as multiplicative ratios, yet divisive ratios are sometimes thought to be more important. For example, 5/3 is sometimes found to be more important than 15. The skew is introduced to address that.
Notably, adopting the Weil height will skew the space by way of adding an extra dimension.
Both the weight and the skew are represented by matrices that can be applied to the mapping. In a more general sense, the distinction may not matter, and they may be collectively called by either part.
Order
The order of the norm determines what is a unit step in the space.
The Euclidean norm aka L^{2} norm resembles real-world distances.
The Minkowskian norm, Manhattan norm or taxicab norm aka L^{1} norm resembles movement of taxicabs in Manhattan – it can only traverse horizontally or vertically. A diagonal movement mounts to two steps.
The Chebyshevian norm aka L^{inifinity} norm is the opposite of the Minkowskian norm – it is the maximum number of steps in any direction, so a diagonal movement is the same as a horizontal or vertical one.
It should be noted that the dual norm of L^{1} is L^{infinity}, and vice versa. Thus, the Minkowskian norm corresponds to the L^{infinity} tuning space, and the Chebyshevian norm corresponds to the L^{1} tuning space. The dual of L^{2} norm is itself, so the Euclidean norm corresponds to Euclidean tuning as one may expect.
Enforcement
Enforcement is the technique where certain intervals are forced to be tuned as desired. The octave is the most common interval subject to enforcement, and is often assumed to be the enforced interval, but other intervals are possible. The two common methods are destretch and constraint.
Destretch
Destretch is a postprocess to enforce a pure interval. The most common destretched tuning is POTE tuning, which works as a quick approximation to the more sophisticated CTWE aka KE tuning.
Constraint
Constraint is a logical method to enforce one or more pure intervals. A pure interval added this way is known as an eigenmonzo. It defines a feasible region for optimization, and the result measured by the original norm remains optimal.
General formulation
In general, the temperament optimization problem (except for the destretch) can be defined as follows. Given a temperament mapping A and the just intonation point (JIP) J_{0}, we specify a weight W, a skew X, and a p-norm. An optional eigenmonzo list B_{C} can be added. The goal is to find the generator list G by
Minimize
[math]\displaystyle \lVert GV - J \rVert_p [/math]
subject to
[math]\displaystyle (GA - J_0)B_{\rm C} = O [/math]
where V is the weight-skewed mapping and J the weight-skewed JIP, found by
[math]\displaystyle \begin{align} V &= AWX \\ J &= J_0 WX \end{align} [/math]
Common tunings
A good number of tunings have been given names. The following table shows some common tuning schemes by weight-skew against the order.
Weight-skew\Order | Chebyshevian (L^{1} tuning) |
Euclidean (L^{2} tuning) |
Minkowskian (L^{infinity} tuning) |
---|---|---|---|
Tenney Tenney-Weil |
TE tuning TWE tuning |
TOP tuning | |
Frobenius Frobenius-Weil |
Frobenius tuning FWE tuning |
||
Benedetti Benedetti-Weil |
BE tuning BWE tuning |
BOP tuning |
Each has a constrained and/or destretched variant. E.g. for TE tuning there is CTE tuning, and for TWE tuning there is CTWE tuning, which is also known as the KE tuning (Kees-Euclidean tuning).
See also
- A Middle Path between Just Intonation and the Equal Temperaments – Part 1 ("middle path") by Paul Erlich
External links
- Prime Based Error and Complexity Measures ("primerr.pdf") by Graham Breed