cobyqa.models.Models

class cobyqa.models.Models(pb, options, penalty)

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.