cobyqa.models.Models#

class cobyqa.models.Models(pb, options)[source]#

Models for a nonlinear optimization problem.

Attributes:
ceq_val

Values of the nonlinear equality constraint functions at the interpolation points.

cub_val

Values of the nonlinear inequality constraint functions at the interpolation points.

fun_val

Values of the objective function at the interpolation points.

interpolation

Interpolation set.

m_nonlinear_eq

Number of nonlinear equality constraints.

m_nonlinear_ub

Number of nonlinear inequality constraints.

n

Dimension of the problem.

npt

Number of interpolation points.

Methods

ceq(x[, mask])

Evaluate the quadratic models of the nonlinear equality functions at a given point.

ceq_curv(v[, mask])

Evaluate the curvature of the quadratic models of the nonlinear equality functions along a given direction.

ceq_grad(x[, mask])

Evaluate the gradients of the quadratic models of the nonlinear equality functions at a given point.

ceq_hess([mask])

Evaluate the Hessian matrices of the quadratic models of the nonlinear equality functions.

ceq_hess_prod(v[, mask])

Evaluate the right product of the Hessian matrices of the quadratic models of the nonlinear equality functions with a given vector.

cub(x[, mask])

Evaluate the quadratic models of the nonlinear inequality functions at a given point.

cub_curv(v[, mask])

Evaluate the curvature of the quadratic models of the nonlinear inequality functions along a given direction.

cub_grad(x[, mask])

Evaluate the gradients of the quadratic models of the nonlinear inequality functions at a given point.

cub_hess([mask])

Evaluate the Hessian matrices of the quadratic models of the nonlinear inequality functions.

cub_hess_prod(v[, mask])

Evaluate the right product of the Hessian matrices of the quadratic models of the nonlinear inequality functions with a given vector.

determinants(x_new[, k_new])

Compute the normalized determinants of the new interpolation systems.

fun(x)

Evaluate the quadratic model of the objective function at a given point.

fun_alt_grad(x)

Evaluate the gradient of the alternative quadratic model of the objective function at a given point.

fun_curv(v)

Evaluate the curvature of the quadratic model of the objective function along a given direction.

fun_grad(x)

Evaluate the gradient of the quadratic model of the objective function at a given point.

fun_hess()

Evaluate the Hessian matrix of the quadratic model of the objective function.

fun_hess_prod(v)

Evaluate the right product of the Hessian matrix of the quadratic model of the objective function with a given vector.

reset_models()

Set the quadratic models of the objective function, nonlinear inequality constraints, and nonlinear equality constraints to the alternative quadratic models.

shift_x_base(new_x_base, options)

Shift the base point without changing the interpolation set.

update_interpolation(k_new, x_new, fun_val, ...)

Update the interpolation set.