Newton-CG trust-region optimization.
||Minimization of scalar function of one or more variables using|
_minimize_trust_ncg(fun, x0, args=tuple, jac=None, hess=None, hessp=None, **trust_region_options)¶
Minimization of scalar function of one or more variables using the Newton conjugate gradient trust-region algorithm.
- initial_trust_radius : float
- Initial trust-region radius.
- max_trust_radius : float
- Maximum value of the trust-region radius. No steps that are longer than this value will be proposed.
- eta : float
- Trust region related acceptance stringency for proposed steps.
- gtol : float
- Gradient norm must be less than gtol before successful termination.
Quadratic subproblem solved by a conjugate gradient method
Solve the subproblem using a conjugate gradient method.
- trust_radius : float
- We are allowed to wander only this far away from the origin.
- p : ndarray
- The proposed step.
- hits_boundary : bool
- True if the proposed step is on the boundary of the trust region.
This is algorithm (7.2) of Nocedal and Wright 2nd edition. Only the function that computes the Hessian-vector product is required. The Hessian itself is not required, and the Hessian does not need to be positive semidefinite.