#include <panoc-alm/util/problem.hpp>

Detailed Description

Problem description for minimization problems.

\[ \begin{aligned} & \underset{x}{\text{minimize}} & & f(x) &&&& f : \mathbb{R}^n \rightarrow \mathbb{R} \\ & \text{subject to} & & \underline{x} \le x \le \overline{x} \\ &&& \underline{z} \le g(x) \le \overline{z} &&&& g : \mathbb{R}^n \rightarrow \mathbb{R}^m \end{aligned} \]

Definition at line 213 of file include/panoc-alm/util/problem.hpp.

Inheritance diagram for ProblemFull:

Collaboration diagram for ProblemFull:

Public Types
using	f_sig = real_t(crvec x)
	Signature of the function that evaluates the cost \( f(x) \). More...

using	grad_f_sig = void(crvec x, rvec grad_fx)
	Signature of the function that evaluates the gradient of the cost function \( \nabla f(x) \). More...

using	g1_sig = void(crvec x, rvec g1x)
	Signature of the function that evaluates the ALM constraints \( g1(x) \). More...

using	grad_g1_prod_sig = void(crvec x, crvec y, rvec grad_g1xy)
	Signature of the function that evaluates the gradient of the ALM constraints times a vector \( \nabla g1(x)\ y \). More...

using	grad_g1i_sig = void(crvec x, unsigned i, rvec grad_g1i)
	Signature of the function that evaluates the gradient of one specific ALM constraint \( \nabla g1_i(x) \). More...

using	g2_sig = void(crvec x, rvec g2x)
	Signature of the function that evaluates the quadratic penalty constraints \( g2(x) \). More...

using	grad_g2_prod_sig = void(crvec x, crvec y, rvec grad_g2xy)
	Signature of the function that evaluates the gradient of the quadratic penalty constraints times a vector \( \nabla g2(x)\ y \). More...

using	grad_g2i_sig = void(crvec x, unsigned i, rvec grad_g2i)
	Signature of the function that evaluates the gradient of one specific quadratic penalty constraints \( \nabla g2_i(x) \). More...

using	hess_L_prod_sig = void(crvec x, crvec y, crvec v, rvec Hv)
	Signature of the function that evaluates the Hessian of the Lagrangian multiplied by a vector \( \nabla_{xx}^2L(x, y)\ v \). More...

using	hess_L_sig = void(crvec x, crvec y, rmat H)
	Signature of the function that evaluates the Hessian of the Lagrangian \( \nabla_{xx}^2L(x, y) \). More...

Public Member Functions
	ProblemFull ()=default

	ProblemFull (unsigned int n, unsigned int m1, unsigned int m2)

	ProblemFull (unsigned n, unsigned int m1, unsigned int m2, Box C, Box D1, Box D2, std::function< f_sig > f, std::function< grad_f_sig > grad_f, std::function< g1_sig > g1, std::function< grad_g1_prod_sig > grad_g1_prod, std::function< grad_g1i_sig > grad_g1i, std::function< g2_sig > g2, std::function< grad_g2_prod_sig > grad_g2_prod, std::function< grad_g2i_sig > grad_g2i, std::function< hess_L_prod_sig > hess_L_prod, std::function< hess_L_sig > hess_L)

Public Attributes
unsigned int	n
	Number of decision variables, dimension of x. More...

unsigned int	m1
	Number of ALM constraints, dimension of g1(x) and z1. More...

unsigned int	m2
	Number of quadratic penalty constraints, dimension of g2(x) and z2. More...

Box	C
	Constraints of the decision variables, \( x \in C \). More...

Box	D1
	ALM constraints, \( g1(x) \in D1 \). More...

Box	D2
	Quadratic penalty constraints, \( g2(x) \in D2 \). More...

std::function< f_sig >	f
	Cost function \( f(x) \). More...

std::function< grad_f_sig >	grad_f
	Gradient of the cost function \( \nabla f(x) \). More...

std::function< g1_sig >	g1
	Constraint function \( g1(x) \). More...

std::function< grad_g1_prod_sig >	grad_g1_prod
	Gradient of the constraint function times vector \( \nabla g1(x)\ y \). More...

std::function< grad_g1i_sig >	grad_g1i
	Gradient of a specific constraint \( \nabla g1_i(x) \). More...

std::function< g2_sig >	g2
	Constraint function \( g2(x) \). More...

std::function< grad_g2_prod_sig >	grad_g2_prod
	Gradient of the constraint function times vector \( \nabla g2(x)\ y \). More...

std::function< grad_g2i_sig >	grad_g2i
	Gradient of a specific constraint \( \nabla g2_i(x) \). More...

std::function< hess_L_prod_sig >	hess_L_prod
	Hessian of the Lagrangian function times vector \( \nabla_{xx}^2 L(x, y)\ v \). More...

std::function< hess_L_sig >	hess_L
	Hessian of the Lagrangian function \( \nabla_{xx}^2 L(x, y) \). More...

Member Typedef Documentation

◆ f_sig

using f_sig = real_t(crvec x)

Signature of the function that evaluates the cost \( f(x) \).

Parameters

[in] x Decision variable \( x \in \mathbb{R}^n \)

Definition at line 224 of file include/panoc-alm/util/problem.hpp.

◆ grad_f_sig

using grad_f_sig = void(crvec x, rvec grad_fx)

Signature of the function that evaluates the gradient of the cost function \( \nabla f(x) \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[out]	grad_fx	Gradient of cost function \( \nabla f(x) \in \mathbb{R}^n \)

Definition at line 231 of file include/panoc-alm/util/problem.hpp.

◆ g1_sig

using g1_sig = void(crvec x, rvec g1x)

Signature of the function that evaluates the ALM constraints \( g1(x) \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[out]	g1x	Value of the constraints \( g1(x) \in \mathbb{R}^m \)

Definition at line 237 of file include/panoc-alm/util/problem.hpp.

◆ grad_g1_prod_sig

using grad_g1_prod_sig = void(crvec x, crvec y, rvec grad_g1xy)

Signature of the function that evaluates the gradient of the ALM constraints times a vector \( \nabla g1(x)\ y \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[in]	y	Vector \( y \in \mathbb{R}^m1 \) to multiply the gradient by
[out]	grad_g1xy	Gradient of the constraints \( \nabla g1(x)\ y \in \mathbb{R}^n \)

Definition at line 248 of file include/panoc-alm/util/problem.hpp.

◆ grad_g1i_sig

using grad_g1i_sig = void(crvec x, unsigned i, rvec grad_g1i)

Signature of the function that evaluates the gradient of one specific ALM constraint \( \nabla g1_i(x) \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[in]	i	Which constraint \( 0 \le i \lt m1 \)
[out]	grad_g1i	Gradient of the constraint \( \nabla g1_i(x) \mathbb{R}^n \)

Definition at line 259 of file include/panoc-alm/util/problem.hpp.

◆ g2_sig

using g2_sig = void(crvec x, rvec g2x)

Signature of the function that evaluates the quadratic penalty constraints \( g2(x) \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[out]	g2x	Value of the constraints \( g2(x) \in \mathbb{R}^m \)

Definition at line 266 of file include/panoc-alm/util/problem.hpp.

◆ grad_g2_prod_sig

using grad_g2_prod_sig = void(crvec x, crvec y, rvec grad_g2xy)

Signature of the function that evaluates the gradient of the quadratic penalty constraints times a vector \( \nabla g2(x)\ y \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[in]	y	Vector \( y \in \mathbb{R}^m \) to multiply the gradient by
[out]	grad_g2xy	Gradient of the constraints \( \nabla g2(x)\ y \in \mathbb{R}^n \)

Definition at line 277 of file include/panoc-alm/util/problem.hpp.

◆ grad_g2i_sig

using grad_g2i_sig = void(crvec x, unsigned i, rvec grad_g2i)

Signature of the function that evaluates the gradient of one specific quadratic penalty constraints \( \nabla g2_i(x) \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[in]	i	Which constraint \( 0 \le i \lt m2 \)
[out]	grad_g2i	Gradient of the constraint \( \nabla g2_i(x) \mathbb{R}^n \)

Definition at line 288 of file include/panoc-alm/util/problem.hpp.

◆ hess_L_prod_sig

using hess_L_prod_sig = void(crvec x, crvec y, crvec v, rvec Hv)

Signature of the function that evaluates the Hessian of the Lagrangian multiplied by a vector \( \nabla_{xx}^2L(x, y)\ v \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[in]	y	Lagrange multipliers \( y \in \mathbb{R}^m \)
[in]	v	Vector to multiply by \( v \in \mathbb{R}^n \)
[out]	Hv	Hessian-vector product \( \nabla_{xx}^2 L(x, y)\ v \in \mathbb{R}^{n} \)

Definition at line 301 of file include/panoc-alm/util/problem.hpp.

◆ hess_L_sig

using hess_L_sig = void(crvec x, crvec y, rmat H)

Signature of the function that evaluates the Hessian of the Lagrangian \( \nabla_{xx}^2L(x, y) \).

Parameters

[in]	x	Decision variable \( x \in \mathbb{R}^n \)
[in]	y	Lagrange multipliers \( y \in \mathbb{R}^m \)
[out]	H	Hessian \( \nabla_{xx}^2 L(x, y) \in \mathbb{R}^{n\times n} \)

Definition at line 310 of file include/panoc-alm/util/problem.hpp.

Constructor & Destructor Documentation

◆ ProblemFull() [1/3]

ProblemFull ( )

default

◆ ProblemFull() [2/3]

ProblemFull	(	unsigned int	n,
		unsigned int	m1,
		unsigned int	m2
	)

inline

Definition at line 335 of file include/panoc-alm/util/problem.hpp.

◆ ProblemFull() [3/3]

ProblemFull	(	unsigned	n,
		unsigned int	m1,
		unsigned int	m2,
		Box	C,
		Box	D1,
		Box	D2,
		std::function< f_sig >	f,
		std::function< grad_f_sig >	grad_f,
		std::function< g1_sig >	g1,
		std::function< grad_g1_prod_sig >	grad_g1_prod,
		std::function< grad_g1i_sig >	grad_g1i,
		std::function< g2_sig >	g2,
		std::function< grad_g2_prod_sig >	grad_g2_prod,
		std::function< grad_g2i_sig >	grad_g2i,
		std::function< hess_L_prod_sig >	hess_L_prod,
		std::function< hess_L_sig >	hess_L
	)