Dog-leg trust-region optimization.
||Minimization of scalar function of one or more variables using|
_minimize_dogleg(fun, x0, args=tuple, jac=None, hess=None, **trust_region_options)¶
Minimization of scalar function of one or more variables using the dog-leg 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 the dogleg method
The Cauchy point is minimal along the direction of steepest descent.
The Newton point is a global minimum of the approximate function.
Minimize a function using the dog-leg trust-region algorithm.
This algorithm requires function values and first and second derivatives. It also performs a costly Hessian decomposition for most iterations, and the Hessian is required to be positive definite.
- 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.
The Hessian is required to be positive definite.
 Jorge Nocedal and Stephen Wright, Numerical Optimization, second edition, Springer-Verlag, 2006, page 73.