/*
* File: SteepestDescentMatrixSolver.java
* Authors: Jeremy D. Wendt
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright 2016, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*/
package gov.sandia.cognition.learning.algorithm.minimization.matrix;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.data.DefaultInputOutputPair;
import gov.sandia.cognition.learning.data.InputOutputPair;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.util.CloneableSerializable;
/**
* Implements a basic Steepest Descent iterative solver for linear systems of
* equations.
*
* @author Jeremy D. Wendt
* @since 4.0.0
*/
@PublicationReference(author = "Jonathan Richard Shewchuk",
title = "An Introduction to the Conjugate Gradient Method Without the Agonizing Pain",
type = PublicationType.WebPage,
year = 1994,
url = "http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf")
final public class SteepestDescentMatrixSolver
extends IterativeMatrixSolver<MatrixVectorMultiplier>
{
/**
* The matrix vector multiplier
*/
private MatrixVectorMultiplier A;
/**
* The per-iteration residual
*/
private Vector residual;
/**
* The current best estimate for x
*/
private Vector x;
/**
* A cross-iteration variable
*/
private double delta;
/**
* Not supported null constructor
*
* @throws UnsupportedOperationException
*/
private SteepestDescentMatrixSolver()
{
super(null, null);
throw new UnsupportedOperationException("Do not call this method.");
}
/**
* Initializes a steepest-descent solver with the minimum values
*
* @param x0 The initial guess for x
* @param rhs The "b" to solve to
*/
public SteepestDescentMatrixSolver(Vector x0,
Vector rhs)
{
super(x0, rhs);
A = null;
residual = null;
x = null;
delta = 0;
}
/**
* Initializes a steepest-descent solver with some additional parameters
*
* @param x0 The initial guess for x
* @param rhs The "b" to solve to
* @param tolerance The minimum tolerance that this must reach before
* stopping (unless maxIterations is exceeded).
*/
public SteepestDescentMatrixSolver(Vector x0,
Vector rhs,
double tolerance)
{
super(x0, rhs, tolerance);
A = null;
residual = null;
x = null;
delta = 0;
}
/**
* Initializes a steepest-descent solver with all user-definable parameters
*
* @param x0 The initial guess for x
* @param rhs The "b" to solve to
* @param tolerance The minimum tolerance that this must reach before
* stopping (unless maxIterations is exceeded).
* @param maxIterations The maximum number of iterations to make
*/
public SteepestDescentMatrixSolver(Vector x0,
Vector rhs,
double tolerance,
int maxIterations)
{
super(x0, rhs, tolerance, maxIterations);
A = null;
residual = null;
x = null;
delta = 0;
}
/**
* Private copy constructor (for clone). Performs a shallow copy of member
* variables.
*
* @param copy The other to copy into this
*/
private SteepestDescentMatrixSolver(SteepestDescentMatrixSolver copy)
{
super(copy);
this.A = copy.A;
this.residual = copy.residual;
this.x = copy.x;
this.delta = copy.delta;
}
@Override
final protected void initializeSolver(MatrixVectorMultiplier function)
{
this.A = function;
x = super.x0;
residual = rhs.minus(function.evaluate(x));
delta = residual.dotProduct(residual);
}
@Override
final protected double iterate()
{
Vector q = A.evaluate(residual);
double alpha = delta / (residual.dotProduct(q));
x.plusEquals(residual.scale(alpha));
if (((iterationCounter + 1) % 50) == 0)
{
residual = rhs.minus(A.evaluate(x));
}
else
{
residual = residual.minus(q.scale(alpha));
}
delta = residual.dotProduct(residual);
return delta;
}
@Override
final protected InputOutputPair<Vector, Vector> completeSolver()
{
InputOutputPair<Vector, Vector> result =
new DefaultInputOutputPair<Vector, Vector>(x0, x);
A = null;
residual = null;
x = null;
delta = 0;
return result;
}
@Override
final public CloneableSerializable clone()
{
return new SteepestDescentMatrixSolver(this);
}
@Override
public boolean equals(Object o)
{
if (!(o instanceof SteepestDescentMatrixSolver))
{
return false;
}
SteepestDescentMatrixSolver other = (SteepestDescentMatrixSolver) o;
if ((A == null) && (other.A != null))
{
return false;
}
else if ((A != null) && !A.equals(other.A))
{
return false;
}
else if ((residual == null) && (other.residual != null))
{
return false;
}
else if ((residual != null) && !residual.equals(other.residual))
{
return false;
}
else if ((x == null) && (other.x != null))
{
return false;
}
else if ((x != null) && !x.equals(other.x))
{
return false;
}
else if (delta != other.delta)
{
return false;
}
return super.equals(o);
}
@Override
public int hashCode()
{
int hash = 1;
hash = hash * 17 + super.hashCode();
hash = hash * 17 + ((A == null) ? 0 : A.hashCode());
hash = hash * 17 + ((residual == null) ? 0 : residual.hashCode());
hash = hash * 17 + ((x == null) ? 0 : x.hashCode());
hash = hash * 17
+ Long.valueOf(Double.doubleToLongBits(delta)).hashCode();
return hash;
}
}