Package org.hipparchus.optim.nonlinear.vector.constrained

Class QuadraticFunction

java.lang.Object

org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction

org.hipparchus.optim.nonlinear.vector.constrained.QuadraticFunction

All Implemented Interfaces:

MultivariateFunction

public class QuadraticFunction
extends TwiceDifferentiableFunction

Given P, Q, d, implements \(\frac{1}{2}x^T P X + Q^T x + d\). The gradient is P x + Q^T, and the Hessian is P

Since:

3.1

Constructor Summary

Constructors

Constructor	Description
QuadraticFunction(double[][] h, double[] c, double d)	Construct quadratic function \(\frac{1}{2}x^T P X + Q^T x + d\).
QuadraticFunction(RealMatrix p, RealVector q, double d)	Construct quadratic function \(\frac{1}{2}x^T P X + Q^T x + d\).

Method Summary

Modifier and Type	Method	Description
int	dim()	Returns the dimensionality of the function domain.
double	getD()	Get constant term.
RealMatrix	getP()	Get square matrix of weights for quadratic terms.
RealVector	getQ()	Get vector of weights for linear terms.
RealVector	gradient(RealVector x)	Returns the gradient of this function at (x)
RealMatrix	hessian(RealVector x)	The Hessian of this function at (x)
double	value(RealVector x)	Returns the value of this function at (x)

Methods inherited from class org.hipparchus.optim.nonlinear.vector.constrained.TwiceDifferentiableFunction

gradient, hessian, value

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

QuadraticFunction

public QuadraticFunction(RealMatrix p,
 RealVector q,
 double d)

Construct quadratic function \(\frac{1}{2}x^T P X + Q^T x + d\).

Parameters:

p - square matrix of weights for quadratic terms. Typically expected to be positive definite or positive semi-definite.

q - vector of weights for linear terms.

d - constant term

QuadraticFunction

public QuadraticFunction(double[][] h,
 double[] c,
 double d)

Construct quadratic function \(\frac{1}{2}x^T P X + Q^T x + d\).

Parameters:

h - square matrix of weights for quadratic terms. Typically expected to be positive definite or positive semi-definite.

c - vector of weights for linear terms.

d - constant term

Method Details

getP

public RealMatrix getP()

Get square matrix of weights for quadratic terms.

Returns:

square matrix of weights for quadratic terms

getQ

public RealVector getQ()

Get vector of weights for linear terms.

Returns:

vector of weights for linear terms

getD

public double getD()

Get constant term.

Returns:

constant term

dim

public int dim()

Returns the dimensionality of the function domain. If dim() returns (n) then this function expects an n-vector as its input.

Specified by:

dim in class TwiceDifferentiableFunction

Returns:

the expected dimension of the function's domain

value

public double value(RealVector x)

Returns the value of this function at (x)

Specified by:

value in class TwiceDifferentiableFunction

Parameters:

x - a point to evaluate this function at.

Returns:

the value of this function at (x)

gradient

public RealVector gradient(RealVector x)

Returns the gradient of this function at (x)

Specified by:

gradient in class TwiceDifferentiableFunction

Parameters:

x - a point to evaluate this gradient at

Returns:

the gradient of this function at (x)

hessian

public RealMatrix hessian(RealVector x)

The Hessian of this function at (x)

Specified by:

hessian in class TwiceDifferentiableFunction

Parameters:

x - a point to evaluate this Hessian at

Returns:

the Hessian of this function at (x)