cobyqa.framework.TrustRegion#

class cobyqa.framework.TrustRegion(pb, options, constants)[source]#

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.

enhance_resolution(options)

Enhance the resolution of the trust-region framework.

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.

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.