User:Sintel/CTE tuning: Difference between revisions

Sintel (talk | contribs)
Created page with "The '''CTE tuning''' ('''constrained Tenney-Euclidean tuning''') is TE tuning under the constraints of some purely tuned intervals (i.e. eigenmonzos). While the TE tun..."
 
Fredg999 category edits (talk | contribs)
m Categories
 
(21 intermediate revisions by one other user not shown)
Line 4: Line 4:


== Definition ==
== Definition ==
Given a temperament [[mapping]] A, the CTE tuning is equivalent to the following optimization problem:  
Given a temperament [[mapping]] M, the CTE tuning is equivalent to the following optimization problem:  


Minimize
<math>
\begin{align}
\underset{g}{\text{minimize}}  & \quad \|  gMW - jW  \|^2  \\
\text{subject to} & \quad (  gM - j )V  = 0 \\
\end{align}
</math>


<math>\lVert GV - J \rVert</math>
where ''g'' is the (unknown) generator list, W the diagonal Tenney-Euclidean weight matrix, ''j'' is the [[JIP]], and V is a matrix obtained by stacking the monzos that we want to be pure. This problem is feasible if rank (V) ≤ rank (M).


subject to
== Computation ==
 
Since this is a convex problem, it can be solved using the method of lagrange multipliers. Let's first simplify:
 
<math>
\begin{align}
A &= (MW)^{\mathsf T}
&b &= (jW)^{\mathsf T} \\
C &= (MV)^{\mathsf T}
&d &= (jV)^{\mathsf T} \\
\end{align}
</math>


<math>(GA - J_0)B = O</math>
The problem then becomes:


where G is the generator list, V = AW the Tenney-weighted temperament mapping, J = J<sub>0</sub>W the Tenney-weighted [[JIP]], and B the monzo list.
<math>
\begin{align}
\underset{g}{\text{minimize}}  & \quad  \left\|  Ag^{\mathsf T} - b  \right\|^2  \\
\text{s.t.} & \quad \phantom{\|} Cg^{\mathsf T} - = 0 \\
\end{align}
</math>


The problem is feasible if
The solution can be found by solving the dual problem:
# rank (B) ≤ rank (A), and
 
# Each column in B and N (A) are [[Wikipedia:linear independence|linearly independent]].
<math>
\begin{bmatrix}
g^{\mathsf T}  \\
\lambda^{\mathsf T}
\end{bmatrix}
=
\begin{bmatrix}
A^{\mathsf T}A & C^{\mathsf T} \\
C & 0
\end{bmatrix}^{-1}
 
\begin{bmatrix}
A^{\mathsf T} b\\
d
\end{bmatrix}
</math>
 
Where we introduced the vector of lagrange multipliers <math>\lambda</math>, with length equal to the number of constraints. The lagrange multipliers have no concrete meaning for the resulting tuning, so they can be ignored.


== Computation ==
As a standard optimization problem, numerous algorithms exist to solve for this tuning, such as [[Wikipedia: Sequential quadratic programming|sequential quadratic programming]], to name one.  
As a standard optimization problem, numerous algorithms exist to solve for this tuning, such as [[Wikipedia: Sequential quadratic programming|sequential quadratic programming]], to name one.  


Line 97: Line 134:
It can be speculated that POTE tends to result in biased tunings whereas CTE less so.
It can be speculated that POTE tends to result in biased tunings whereas CTE less so.


[[Category:Regular temperament theory]]
[[Category:Regular temperament tuning]]
[[Category:Terms]]
[[Category:Terms]]
[[Category:Math]]
[[Category:Math]]
[[Category:Tuning]]
[[Category:Tuning technique]]