cobyqa.framework.TrustRegion

class cobyqa.framework.TrustRegion(pb, options)

Trust-region framework.

Attributes:

best_index

Index of the best interpolation point.

ceq_best

Values of the nonlinear equality constraints at x_best.

cub_best

Values of the nonlinear inequality constraints at x_best.

fun_best

Value of the objective function at x_best.

m_linear_eq

Number of linear equality constraints.

m_linear_ub

Number of linear inequality constraints.

m_nonlinear_eq

Number of nonlinear equality constraints.

m_nonlinear_ub

Number of nonlinear inequality constraints.

models

Models of the objective function and constraints.

n

Number of variables.

penalty

Penalty parameter.

radius

Trust-region radius.

resolution

Resolution of the trust-region framework.

x_best

Best interpolation point.

Methods

decrease_penalty()	Decrease the penalty parameter.
get_constraint_linearizations(x)	Get the linearizations of the constraints at a given point.
get_geometry_step(k_new, options)	Get the geometry-improving step.
get_index_to_remove([x_new])	Get the index of the interpolation point to remove.
get_reduction_ratio(step, fun_val, cub_val, ...)	Get the reduction ratio.
get_second_order_correction_step(step, options)	Get the second-order correction step.
get_trust_region_step(options)	Get the trust-region step.
increase_penalty(step)	Increase the penalty parameter.
lag_model(x)	Evaluate the Lagrangian model at a given point.
lag_model_curv(v)	Evaluate the curvature of the Lagrangian model along a given direction.
lag_model_grad(x)	Evaluate the gradient of the Lagrangian model at a given point.
lag_model_hess()	Evaluate the Hessian matrix of the Lagrangian model at a given point.
lag_model_hess_prod(v)	Evaluate the right product of the Hessian matrix of the Lagrangian model with a given vector.
merit(x[, fun_val, cub_val, ceq_val])	Evaluate the merit function at a given point.
reduce_resolution(options)	Reduce the resolution of the trust-region framework.
set_best_index()	Set the index of the best point.
set_multipliers(x)	Set the Lagrange multipliers.
shift_x_base(options)	Shift the base point to x_best.
sqp_ceq(step)	Evaluate the linearization of the nonlinear equality constraints.
sqp_cub(step)	Evaluate the linearization of the nonlinear inequality constraints.
sqp_fun(step)	Evaluate the objective function of the SQP subproblem.
update_radius(step, ratio)	Update the trust-region radius.