Package org.hipparchus.optim.nonlinear.vector.leastsquares

Class SequentialGaussNewtonOptimizer

java.lang.Object

org.hipparchus.optim.nonlinear.vector.leastsquares.SequentialGaussNewtonOptimizer

All Implemented Interfaces:

LeastSquaresOptimizer

public class SequentialGaussNewtonOptimizer
extends Object
implements LeastSquaresOptimizer

Sequential Gauss-Newton least-squares solver.

This class solve a least-square problem by solving the normal equations of the linearized problem at each iteration.

Nested Class Summary

Nested classes/interfaces inherited from interface org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer

LeastSquaresOptimizer.Optimum

Constructor Summary

Constructors

Constructor	Description
SequentialGaussNewtonOptimizer()	Create a sequential Gauss Newton optimizer.
SequentialGaussNewtonOptimizer(MatrixDecomposer decomposer, boolean formNormalEquations, LeastSquaresProblem.Evaluation evaluation)	Create a sequential Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.

Method Summary

Modifier and Type	Method	Description
MatrixDecomposer	getDecomposer()	Get the matrix decomposition algorithm.
LeastSquaresProblem.Evaluation	getOldEvaluation()	Get the previous evaluation used by the optimizer.
boolean	isFormNormalEquations()	Get if the normal equations are explicitly formed.
LeastSquaresOptimizer.Optimum	optimize(LeastSquaresProblem lsp)	Solve the non-linear least squares problem.
String	toString()
SequentialGaussNewtonOptimizer	withAPrioriData(RealVector aPrioriState, RealMatrix aPrioriCovariance)	Configure from a priori state and covariance.
SequentialGaussNewtonOptimizer	withAPrioriData(RealVector aPrioriState, RealMatrix aPrioriCovariance, double relativeSymmetryThreshold, double absolutePositivityThreshold)	Configure from a priori state and covariance.
SequentialGaussNewtonOptimizer	withDecomposer(MatrixDecomposer newDecomposer)	Configure the matrix decomposition algorithm.
SequentialGaussNewtonOptimizer	withEvaluation(LeastSquaresProblem.Evaluation previousEvaluation)	Configure the previous evaluation used by the optimizer.
SequentialGaussNewtonOptimizer	withFormNormalEquations(boolean newFormNormalEquations)	Configure if the normal equations should be explicitly formed.

Methods inherited from class java.lang.Object

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

Constructor Detail

SequentialGaussNewtonOptimizer

public SequentialGaussNewtonOptimizer()

Create a sequential Gauss Newton optimizer.

The default for the algorithm is to use QR decomposition, not form normal equations and have no previous evaluation

SequentialGaussNewtonOptimizer

public SequentialGaussNewtonOptimizer(MatrixDecomposer decomposer,
                                      boolean formNormalEquations,
                                      LeastSquaresProblem.Evaluation evaluation)

Create a sequential Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.

The decomposer is used to solve J^TJx=J^Tr.

Parameters:

decomposer - the decomposition algorithm to use.

formNormalEquations - whether the normal equations should be explicitly formed. If true then decomposer is used to solve J^TJx=J^Tr, otherwise decomposer is used to solve Jx=r. If decomposer can only solve square systems then this parameter should be true.

evaluation - old evaluation previously computed, null if there are no previous evaluations.

Method Detail

getDecomposer

public MatrixDecomposer getDecomposer()

Get the matrix decomposition algorithm.

Returns:

the decomposition algorithm.

withDecomposer

public SequentialGaussNewtonOptimizer withDecomposer(MatrixDecomposer newDecomposer)

Configure the matrix decomposition algorithm.

Parameters:

newDecomposer - the decomposition algorithm to use.

Returns:

a new instance.

isFormNormalEquations

public boolean isFormNormalEquations()

Get if the normal equations are explicitly formed.

Returns:

if the normal equations should be explicitly formed. If true then decomposer is used to solve J^TJx=J^Tr, otherwise decomposer is used to solve Jx=r.

withFormNormalEquations

public SequentialGaussNewtonOptimizer withFormNormalEquations(boolean newFormNormalEquations)

Configure if the normal equations should be explicitly formed.

Parameters:

newFormNormalEquations - whether the normal equations should be explicitly formed. If true then decomposer is used to solve J^TJx=J^Tr, otherwise decomposer is used to solve Jx=r. If decomposer can only solve square systems then this parameter should be true.

Returns:

a new instance.

getOldEvaluation

public LeastSquaresProblem.Evaluation getOldEvaluation()

Get the previous evaluation used by the optimizer.

Returns:

the previous evaluation.

withEvaluation

public SequentialGaussNewtonOptimizer withEvaluation(LeastSquaresProblem.Evaluation previousEvaluation)

Configure the previous evaluation used by the optimizer.

This building method uses a complete evaluation to retrieve a priori data. Note that as withAPrioriData(RealVector, RealMatrix) generates a fake evaluation and calls this method, either withAPrioriData(RealVector, RealMatrix) or withEvaluation(LeastSquaresProblem.Evaluation) should be called, but not both as the last one called will override the previous one.

Parameters:

previousEvaluation - the previous evaluation used by the optimizer.

Returns:

a new instance.

withAPrioriData

public SequentialGaussNewtonOptimizer withAPrioriData(RealVector aPrioriState,
                                                      RealMatrix aPrioriCovariance)

Configure from a priori state and covariance.

This building method generates a fake evaluation and calls withEvaluation(LeastSquaresProblem.Evaluation), so either withAPrioriData(RealVector, RealMatrix) or withEvaluation(LeastSquaresProblem.Evaluation) should be called, but not both as the last one called will override the previous one.

A Cholesky decomposition is used to compute the weighted jacobian from the a priori covariance. This method uses the default thresholds of the decomposition.

Parameters:

aPrioriState - a priori state to use

aPrioriCovariance - a priori covariance to use

Returns:

a new instance.

See Also:

withAPrioriData(RealVector, RealMatrix, double, double)

withAPrioriData

public SequentialGaussNewtonOptimizer withAPrioriData(RealVector aPrioriState,
                                                      RealMatrix aPrioriCovariance,
                                                      double relativeSymmetryThreshold,
                                                      double absolutePositivityThreshold)

Configure from a priori state and covariance.

This building method generates a fake evaluation and calls withEvaluation(LeastSquaresProblem.Evaluation), so either withAPrioriData(RealVector, RealMatrix) or withEvaluation(LeastSquaresProblem.Evaluation) should be called, but not both as the last one called will override the previous one.

A Cholesky decomposition is used to compute the weighted jacobian from the a priori covariance.

Parameters:

aPrioriState - a priori state to use

aPrioriCovariance - a priori covariance to use

relativeSymmetryThreshold - Cholesky decomposition threshold above which off-diagonal elements are considered too different and matrix not symmetric

absolutePositivityThreshold - Cholesky decomposition threshold below which diagonal elements are considered null and matrix not positive definite

Returns:

a new instance.

Since:

2.3

optimize

public LeastSquaresOptimizer.Optimum optimize(LeastSquaresProblem lsp)

Solve the non-linear least squares problem.

Specified by:

optimize in interface LeastSquaresOptimizer

Parameters:

lsp - the problem definition, including model function and convergence criteria.

Returns:

The optimum.

toString

public String toString()

Overrides:

toString in class Object