pygsti.optimize.customcg
A custom conjugate gradient descent algorithm
Module Contents
Functions
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Custom conjugate-gradient (CG) routine for maximizing a function. |
- pygsti.optimize.customcg.fmax_cg(f, x0, maxiters=100, tol=1e-08, dfdx_and_bdflag=None, xopt=None)
Custom conjugate-gradient (CG) routine for maximizing a function.
This function runs slower than scipy.optimize’s ‘CG’ method, but doesn’t give up or get stuck as easily, and so sometimes can be a better option.
Parameters
- ffunction
The function to optimize
- x0numpy array
The starting point (argument to fn).
- maxitersint, optional
Maximum iterations.
- tolfloat, optional
Tolerace for convergence (compared to absolute difference in f)
- dfdx_and_bdflagfunction, optional
Function to compute jacobian of f as well as a boundary-flag.
- xoptnumpy array, optional
Used for debugging, output can be printed relating current optimum relative xopt, assumed to be a known good optimum.
Returns
- scipy.optimize.Result object
Includes members ‘x’, ‘fun’, ‘success’, and ‘message’. Note: returns the negated maximum in ‘fun’ in order to conform to the return value of other minimization routines.