A globally convergent Newton's method with inequality constraints c(x) >= 0. More...

#include <Newton.h>

Inheritance diagram for Optimization::ConstrainedNewtonRoot:

Public Member Functions
	ConstrainedNewtonRoot (VectorFieldFunction func, VectorFieldFunction c)

bool	GlobalSolve (int &iters, ConvergenceResult *res=NULL)

ConvergenceResult	SolveConstrained (int &iters)

ConvergenceResult	SolveConstrained2 (int &iters)

ConvergenceResult	SolveConstrained2_Sparse (int &iters)

ConvergenceResult	SolveConstrained_SLP (int &iters)

virtual Real	Merit ()

Public Member Functions inherited from Optimization::NewtonRoot
	NewtonRoot (VectorFieldFunction *func)

bool	GlobalSolve (int &iters, ConvergenceResult *res=NULL)

ConvergenceResult	Solve (int &iters)

ConvergenceResult	Solve_Sparse (int &iters)

bool	LineMinimization (const Vector &g, const Vector &p, Real *f)

Real	MaxDistance (const Vector &x)

virtual bool	SolveUnderconstrainedLS (const Matrix &A, const Vector &b, Vector &x)

virtual bool	SolveUnderconstrainedLS (const SparseMatrix &A, const Vector &b, Vector &x)

Public Attributes
VectorFieldFunction *	c

Real	tolc

Vector	cx

std::vector< int >	activeSetC

std::vector< int >	activeSetBound

Matrix	A

Vector	rhs

Public Attributes inherited from Optimization::NewtonRoot
Vector	x

VectorFieldFunction *	func

Real	tolf

Real	tolmin

Real	tolx

Real	stepMax
	maximum distance to step

Real	lambda
	damped-least-squares constant

Vector	bmin

Vector	bmax
	optional bound constraints

bool	sparse
	set to true if should use a sparse least-squares solver.

Vector	bias
	set this to a bias vector to solve for min \|\|x-bias\|\| s.t. func(x)=0

int	verbose

int	debug

RobustSVD< Real >	svd

Vector	fx

Vector	g

Vector	p

Vector	xold

Matrix	fJx

Detailed Description

A globally convergent Newton's method with inequality constraints c(x) >= 0.

The documentation for this class was generated from the following files:

Public Member Functions