net.finmath.optimizer.StochasticPathwiseLevenbergMarquardtAD Maven / Gradle / Ivy
/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 16.06.2006
*/
package net.finmath.optimizer;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ExecutorService;
import net.finmath.montecarlo.automaticdifferentiation.RandomVariableDifferentiable;
import net.finmath.stochastic.RandomVariable;
/**
* This class implements a stochastic Levenberg Marquardt non-linear least-squares fit
* algorithm.
*
* The design avoids the need to define the objective function as a
* separate class. The objective function is defined by overriding a class
* method, see the sample code below.
*
*
*
* The Levenberg-Marquardt solver is implemented in using multi-threading.
* The calculation of the derivatives (in case a specific implementation of
* {@code setDerivatives(RandomVariable[] parameters, RandomVariable[][] derivatives)} is not
* provided) may be performed in parallel by setting the parameter numberOfThreads.
*
*
*
* To use the solver inherit from it and implement the objective function as
* {@code setValues(RandomVariable[] parameters, RandomVariable[] values)} where values has
* to be set to the value of the objective functions for the given parameters.
*
* You may also provide an a derivative for your objective function by
* additionally overriding the function {@code setDerivatives(RandomVariable[] parameters, RandomVariable[][] derivatives)},
* otherwise the solver will calculate the derivative via finite differences.
*
*
* To reject a point, it is allowed to set an element of values to {@link java.lang.Double#NaN}
* in the implementation of {@code setValues(RandomVariable[] parameters, RandomVariable[] values)}.
* Put differently: The solver handles NaN values in values as an error larger than
* the current one (regardless of the current error) and rejects the point.
*
* Note, however, that is is an error if the initial parameter guess results in an NaN value.
* That is, the solver should be initialized with an initial parameter in an admissible region.
*
*
* The following simple example finds a solution for the equation
*
* Sample linear system of equations.
*
* 0.0 * x1 + 1.0 * x2 = 5.0
*
* 2.0 * x1 + 1.0 * x2 = 10.0
*
*
*
*
* LevenbergMarquardt optimizer = new LevenbergMarquardt() {
* // Override your objective function here
* public void setValues(RandomVariable[] parameters, RandomVariable[] values) {
* values[0] = parameters[0] * 0.0 + parameters[1];
* values[1] = parameters[0] * 2.0 + parameters[1];
* }
* };
*
* // Set solver parameters
* optimizer.setInitialParameters(new RandomVariable[] { 0, 0 });
* optimizer.setWeights(new RandomVariable[] { 1, 1 });
* optimizer.setMaxIteration(100);
* optimizer.setTargetValues(new RandomVariable[] { 5, 10 });
*
* optimizer.run();
*
* RandomVariable[] bestParameters = optimizer.getBestFitParameters();
*
*
*
* See the example in the main method below.
*
*
* The class can be initialized to use a multi-threaded valuation. If initialized
* this way the implementation of setValues must be thread-safe.
* The solver will evaluate the gradient of the value vector in parallel, i.e.,
* use as many threads as the number of parameters.
*
*
* Note: Iteration steps will be logged (java.util.logging) with LogLevel.FINE
*
* @author Christian Fries
* @version 1.6
*/
public abstract class StochasticPathwiseLevenbergMarquardtAD extends net.finmath.optimizer.StochasticPathwiseLevenbergMarquardt {
/**
*
*/
private static final long serialVersionUID = -8852002990042152135L;
public StochasticPathwiseLevenbergMarquardtAD(final List initialParameters, final List targetValues, final int maxIteration, final ExecutorService executorService) {
super(initialParameters, targetValues, maxIteration, executorService);
}
public StochasticPathwiseLevenbergMarquardtAD(final List initialParameters, final List targetValues, final int maxIteration, final int numberOfThreads) {
super(initialParameters, targetValues, maxIteration, numberOfThreads);
}
public StochasticPathwiseLevenbergMarquardtAD(final RandomVariable[] initialParameters, final RandomVariable[] targetValues, final int maxIteration, final int numberOfThreads) {
super(initialParameters, targetValues, maxIteration, numberOfThreads);
}
public StochasticPathwiseLevenbergMarquardtAD(final RandomVariable[] initialParameters,
final RandomVariable[] targetValues, final RandomVariable[] weights,
final RandomVariable[] parameterSteps, final int maxIteration, final RandomVariable errorTolerance,
final ExecutorService executorService) {
super(initialParameters, targetValues, weights, parameterSteps, maxIteration, errorTolerance, executorService);
}
@Override
protected void prepareAndSetValues(final RandomVariable[] parameters, final RandomVariable[] values) throws SolverException {
/*
* Small modification to avoid growing operator trees.
*/
for(int i=0; i gradient = ((RandomVariableDifferentiable)values[valueIndex]).getGradient();
for (int parameterIndex = 0; parameterIndex < parameters.length; parameterIndex++) {
derivatives[parameterIndex][valueIndex] = gradient.get(((RandomVariableDifferentiable)parameters[parameterIndex]).getID());
}
}
}
else {
setDerivatives(parameters, derivatives);
}
}
}