Module net.finmath.lib
Package net.finmath.optimizer

Class StochasticLevenbergMarquardtAD

java.lang.Object
net.finmath.optimizer.StochasticLevenbergMarquardt
net.finmath.optimizer.StochasticLevenbergMarquardtAD
All Implemented Interfaces:
Serializable, Cloneable, StochasticOptimizer
public abstract class StochasticLevenbergMarquardtAD extends StochasticLevenbergMarquardt
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 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 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 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 Double.NaN in the implementation of 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
Version:
1.6
Author:
Christian Fries
See Also:
Serialized Form