pygsti.optimize.fmax_cg¶
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pygsti.optimize.
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: - fn (function) – The function to minimize.
- x0 (numpy array) – The starting point (argument to fn).
- maxiters (int, optional) – Maximum iterations.
- tol (float, optional) – Tolerace for convergence (compared to absolute difference in f)
- dfdx_and_bdflag (function, optional) – Function to compute jacobian of f as well as a boundary-flag.
- xopt (numpy array, optional) – Used for debugging, output can be printed relating current optimum relative xopt, assumed to be a known good optimum.
Returns: 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.
Return type: scipy.optimize.Result object