org.apache.commons.math3.optim.nonlinear.vector.jacobian

Class AbstractLeastSquaresOptimizer

java.lang.Object

org.apache.commons.math3.optim.BaseOptimizer<PAIR>

org.apache.commons.math3.optim.BaseMultivariateOptimizer<PointVectorValuePair>

org.apache.commons.math3.optim.nonlinear.vector.MultivariateVectorOptimizer

org.apache.commons.math3.optim.nonlinear.vector.JacobianMultivariateVectorOptimizer

org.apache.commons.math3.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer

Direct Known Subclasses:

GaussNewtonOptimizer, LevenbergMarquardtOptimizer

Deprecated.

All classes and interfaces in this package are deprecated. The optimizers that were provided here were moved to the org.apache.commons.math3.fitting.leastsquares package (cf. MATH-1008).

@Deprecated
public abstract class AbstractLeastSquaresOptimizer
extends JacobianMultivariateVectorOptimizer

Base class for implementing least-squares optimizers. It provides methods for error estimation.

Since:

3.1

Method Summary

Modifier and Type	Method and Description
double[][]	computeCovariances(double[] params, double threshold) Deprecated. Get the covariance matrix of the optimized parameters.
double[]	computeSigma(double[] params, double covarianceSingularityThreshold) Deprecated. Computes an estimate of the standard deviation of the parameters.
double	getChiSquare() Deprecated. Get a Chi-Square-like value assuming the N residuals follow N distinct normal distributions centered on 0 and whose variances are the reciprocal of the weights.
double	getRMS() Deprecated. Gets the root-mean-square (RMS) value.
RealMatrix	getWeightSquareRoot() Deprecated. Gets the square-root of the weight matrix.
PointVectorValuePair	optimize(OptimizationData... optData) Deprecated. Stores data and performs the optimization.

Methods inherited from class org.apache.commons.math3.optim.nonlinear.vector.MultivariateVectorOptimizer

getTarget, getTargetSize, getWeight

Methods inherited from class org.apache.commons.math3.optim.BaseMultivariateOptimizer

getLowerBound, getStartPoint, getUpperBound

Methods inherited from class org.apache.commons.math3.optim.BaseOptimizer

getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, optimize

Methods inherited from class java.lang.Object

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

Method Detail

getRMS

public double getRMS()

Deprecated.

Gets the root-mean-square (RMS) value. The RMS the root of the arithmetic mean of the square of all weighted residuals. This is related to the criterion that is minimized by the optimizer as follows: If c if the criterion, and n is the number of measurements, then the RMS is sqrt (c/n).

Returns:

the RMS value.

getChiSquare

public double getChiSquare()

Deprecated.

Get a Chi-Square-like value assuming the N residuals follow N distinct normal distributions centered on 0 and whose variances are the reciprocal of the weights.

Returns:

chi-square value

getWeightSquareRoot

public RealMatrix getWeightSquareRoot()

Deprecated.

Gets the square-root of the weight matrix.

Returns:

the square-root of the weight matrix.

computeCovariances

public double[][] computeCovariances(double[] params,
                                     double threshold)

Deprecated.

Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of the JTJ matrix, where J is the Jacobian matrix. The threshold parameter is a way for the caller to specify that the result of this computation should be considered meaningless, and thus trigger an exception.

Parameters:

params - Model parameters.

threshold - Singularity threshold.

Returns:

the covariance matrix.

Throws:

SingularMatrixException - if the covariance matrix cannot be computed (singular problem).

computeSigma

public double[] computeSigma(double[] params,
                             double covarianceSingularityThreshold)

Deprecated.

Computes an estimate of the standard deviation of the parameters. The returned values are the square root of the diagonal coefficients of the covariance matrix, sd(a[i]) ~= sqrt(C[i][i]), where a[i] is the optimized value of the i-th parameter, and C is the covariance matrix.

Parameters:

params - Model parameters.

covarianceSingularityThreshold - Singularity threshold (see computeCovariances).

Returns:

an estimate of the standard deviation of the optimized parameters

Throws:

SingularMatrixException - if the covariance matrix cannot be computed.

optimize

public PointVectorValuePair optimize(OptimizationData... optData)
                              throws TooManyEvaluationsException

Deprecated.

Stores data and performs the optimization.

The list of parameters is open-ended so that sub-classes can extend it with arguments specific to their concrete implementations.

When the method is called multiple times, instance data is overwritten only when actually present in the list of arguments: when not specified, data set in a previous call is retained (and thus is optional in subsequent calls).

Important note: Subclasses must override BaseOptimizer.parseOptimizationData(OptimizationData[]) if they need to register their own options; but then, they must also call super.parseOptimizationData(optData) within that method.

Overrides:

optimize in class JacobianMultivariateVectorOptimizer

Parameters:

optData - Optimization data. In addition to those documented in JacobianMultivariateVectorOptimizer, this method will register the following data:

• Weight

Returns:

a point/value pair that satisfies the convergence criteria.

Throws:

TooManyEvaluationsException - if the maximal number of evaluations is exceeded.

DimensionMismatchException - if the initial guess, target, and weight arguments have inconsistent dimensions.