- class CG(maxiter=20, disp=False, gtol=1e-05, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)¶
Conjugate Gradient optimizer.
CG is an algorithm for the numerical solution of systems of linear equations whose matrices are symmetric and positive-definite. It is an iterative algorithm in that it uses an initial guess to generate a sequence of improving approximate solutions for a problem, in which each approximation is derived from the previous ones. It is often used to solve unconstrained optimization problems, such as energy minimization.
Uses scipy.optimize.minimize CG. For further detail, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
int) – Maximum number of iterations to perform.
bool) – Set to True to print convergence messages.
float) – Gradient norm must be less than gtol before successful termination.
float]) – Tolerance for termination.
float) – If jac is approximated, use this value for the step size.
dict]) – A dictionary of solver options.
int) – Max number of default gradient evaluations performed simultaneously.
kwargs – additional kwargs for scipy.optimize.minimize.
Return support level dictionary
We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
Minimize the scalar function.
Print algorithm-specific options.
Set max evals grouped
Sets or updates values in the options dictionary.
Wrap the function to implicitly inject the args at the call of the function.
Returns bounds support level
Returns gradient support level
Returns initial point support level
Returns is bounds ignored
Returns is bounds required
Returns is bounds supported
Returns is gradient ignored
Returns is gradient required
Returns is gradient supported
Returns is initial point ignored
Returns is initial point required
Returns is initial point supported
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