Apache Commons Math 3.6.1 · org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer

Modifier and Type	Field and Description
private ConvergenceChecker<PointVectorValuePair>	checker Deprecated as a consequence of `ConvergenceChecker` being deprecated. Convergence checker.
protected final Incrementor	evaluations Deprecated as a consequence of `Incrementor` being deprecated. Evaluations counter.
private FUNC	function Objective function.
private double[]	start Initial guess.
private double[]	target Target value for the objective functions at optimum.
private double[]	weight Deprecated Weight for the least squares cost computation.
private RealMatrix	weightMatrix Weight matrix.

Access	Constructor and Description
protected	BaseAbstractMultivariateVectorOptimizer() Deprecated See `SimpleVectorValueChecker#SimpleVectorValueChecker()` Simple constructor with default settings.
protected	BaseAbstractMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> Convergence checker. checker) Deprecated as a consequence of `ConvergenceChecker` being deprecated.

Method Summary

Modifier and Type	Method and Description
private void	checkParameters() Check parameters consistency.
protected double[]	Returns: the objective function value at the specified point. computeObjectiveValue(double[] Point at which the objective function must be evaluated. point) Compute the objective function value.
protected abstract PointVectorValuePair	Returns: the point/value pair giving the optimal value for the objective function. doOptimize() Deprecated as a consequence of `PointVectorValuePair` being deprecated. Perform the bulk of the optimization algorithm.
public ConvergenceChecker<PointVectorValuePair>	getConvergenceChecker() Implements org.apache.commons.math3.optimization.BaseOptimizer.getConvergenceChecker. Deprecated as a consequence of `ConvergenceChecker` being deprecated. Get the convergence checker.
public int	getEvaluations() Implements org.apache.commons.math3.optimization.BaseOptimizer.getEvaluations. Get the number of evaluations of the objective function.
public int	getMaxEvaluations() Implements org.apache.commons.math3.optimization.BaseOptimizer.getMaxEvaluations. Get the maximal number of function evaluations.
protected FUNC	Returns: the objective vector function. getObjectiveFunction() Gets the objective vector function.
public double[]	Returns: the initial guess. getStartPoint() Gets the initial values of the optimized parameters.
public double[]	Returns: the target values. getTarget() Gets the observed values to be matched by the objective vector function.
protected double[]	Returns: a reference to the `array`. getTargetRef() Deprecated As of 3.1.
public RealMatrix	Returns: the weight matrix. getWeight() Gets the weight matrix of the observations.
protected double[]	Returns: a reference to the `array`. getWeightRef() Deprecated As of 3.1.
public PointVectorValuePair	optimize(int Maximum number of function evaluations. maxEval, FUNC Objective function. f, double[] Target value for the objective functions at optimum. t, double[] Weights for the least squares cost computation. w, double[] Start point for optimization. startPoint) Implements org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize. Deprecated As of 3.1. Optimize an objective function.
protected PointVectorValuePair	Returns: the point/value pair giving the optimal value of the objective function. optimize(int Allowed number of evaluations of the objective function. maxEval, FUNC Objective function. f, OptimizationData... Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess` optData) Deprecated as a consequence of `OptimizationData` and `PointVectorValuePair` being deprecated. Optimize an objective function.
protected PointVectorValuePair	Returns: the point/value pair giving the optimal value for objective function. optimizeInternal(final int Maximum number of function evaluations. maxEval, final FUNC Objective function. f, final double[] Target value for the objective functions at optimum. t, final double[] Weights for the least squares cost computation. w, final double[] Start point for optimization. startPoint) Deprecated As of 3.1. Optimize an objective function.
protected PointVectorValuePair	Returns: the point/value pair giving the optimal value of the objective function. optimizeInternal(int Allowed number of evaluations of the objective function. maxEval, FUNC Objective function. f, OptimizationData... Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess` optData) Deprecated as a consequence of `OptimizationData` and `PointVectorValuePair` being deprecated. Optimize an objective function.
private void	parseOptimizationData(OptimizationData... Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess` optData) Deprecated as a consequence of `OptimizationData` being deprecated. Scans the list of (required and optional) optimization data that characterize the problem.
protected void	setUp() Method which a subclass must override whenever its internal state depend on the `input` parsed by this base class.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

checker	back to summary
private ConvergenceChecker<PointVectorValuePair> checker Deprecated as a consequence of `ConvergenceChecker` being deprecated. See corresponding docs for further information. Convergence checker.

evaluations	back to summary
protected final Incrementor evaluations Deprecated as a consequence of `Incrementor` being deprecated. See corresponding docs for further information. Evaluations counter.

function	back to summary
private FUNC function Objective function.

start	back to summary
private double[] start Initial guess.

target	back to summary
private double[] target Target value for the objective functions at optimum.

weight	back to summary
private double[] weight Deprecated Weight for the least squares cost computation. Annotations @Deprecated

weightMatrix	back to summary
private RealMatrix weightMatrix Weight matrix.

BaseAbstractMultivariateVectorOptimizer	back to summary
protected BaseAbstractMultivariateVectorOptimizer() Deprecated See `SimpleVectorValueChecker#SimpleVectorValueChecker()` Simple constructor with default settings. The convergence check is set to a `SimpleVectorValueChecker`. Annotations @Deprecated

BaseAbstractMultivariateVectorOptimizer	back to summary
protected BaseAbstractMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) Deprecated as a consequence of `ConvergenceChecker` being deprecated. See corresponding docs for further information. Parameters checker:ConvergenceChecker<PointVectorValuePair> Convergence checker.

Method Detail

checkParameters back to summary

checkParameters	back to summary
private void checkParameters() Check parameters consistency. Exceptions DimensionMismatchException: if `target` and `weightMatrix` have inconsistent dimensions.

private void checkParameters()

Check parameters consistency.

Exceptions

DimensionMismatchException:: if target and weightMatrix have inconsistent dimensions.

computeObjectiveValue	back to summary
protected double[] computeObjectiveValue(double[] point) Compute the objective function value. Parameters point:double[] Point at which the objective function must be evaluated. Returns:double[] the objective function value at the specified point. Exceptions TooManyEvaluationsException: if the maximal number of evaluations is exceeded.

computeObjectiveValue

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protected double[] computeObjectiveValue(double[] point)

Compute the objective function value.

Parameters

point:double[]: Point at which the objective function must be evaluated.

Returns:double[]

the objective function value at the specified point.

Exceptions

TooManyEvaluationsException:: if the maximal number of evaluations is exceeded.

doOptimize back to summary

doOptimize	back to summary
protected abstract PointVectorValuePair doOptimize() Deprecated as a consequence of `PointVectorValuePair` being deprecated. See corresponding docs for further information. Perform the bulk of the optimization algorithm. Returns:PointVectorValuePair the point/value pair giving the optimal value for the objective function.

protected abstract PointVectorValuePair doOptimize()

Deprecated

as a consequence of PointVectorValuePair being deprecated. See corresponding docs for further information.

Perform the bulk of the optimization algorithm.

Returns:PointVectorValuePair: the point/value pair giving the optimal value for the objective function.

getConvergenceChecker back to summary

getConvergenceChecker	back to summary
public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() Implements org.apache.commons.math3.optimization.BaseOptimizer.getConvergenceChecker. Doc from org.apache.commons.math3.optimization.BaseOptimizer.getConvergenceChecker. Deprecated as a consequence of `ConvergenceChecker` being deprecated. See corresponding docs for further information. Get the convergence checker. Returns:ConvergenceChecker<PointVectorValuePair> the object used to check for convergence.

public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker()

Implements org.apache.commons.math3.optimization.BaseOptimizer.getConvergenceChecker.

Doc from org.apache.commons.math3.optimization.BaseOptimizer.getConvergenceChecker.

Deprecated

as a consequence of ConvergenceChecker being deprecated. See corresponding docs for further information.

Get the convergence checker.

Returns:ConvergenceChecker<PointVectorValuePair>: the object used to check for convergence.

getEvaluations back to summary

getEvaluations	back to summary
public int getEvaluations() Implements org.apache.commons.math3.optimization.BaseOptimizer.getEvaluations. Doc from org.apache.commons.math3.optimization.BaseOptimizer.getEvaluations. Get the number of evaluations of the objective function. The number of evaluations corresponds to the last call to the `optimize` method. It is 0 if the method has not been called yet. Returns:int the number of evaluations of the objective function.

public int getEvaluations()

Implements org.apache.commons.math3.optimization.BaseOptimizer.getEvaluations.

Doc from org.apache.commons.math3.optimization.BaseOptimizer.getEvaluations.

Get the number of evaluations of the objective function. The number of evaluations corresponds to the last call to the optimize method. It is 0 if the method has not been called yet.

Returns:int: the number of evaluations of the objective function.

getMaxEvaluations	back to summary
public int getMaxEvaluations() Implements org.apache.commons.math3.optimization.BaseOptimizer.getMaxEvaluations. Doc from org.apache.commons.math3.optimization.BaseOptimizer.getMaxEvaluations. Get the maximal number of function evaluations. Returns:int the maximal number of function evaluations.

getMaxEvaluations

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public int getMaxEvaluations()

Implements org.apache.commons.math3.optimization.BaseOptimizer.getMaxEvaluations.

Doc from org.apache.commons.math3.optimization.BaseOptimizer.getMaxEvaluations.

Get the maximal number of function evaluations.

Returns:int: the maximal number of function evaluations.

getObjectiveFunction	back to summary
protected FUNC getObjectiveFunction() Gets the objective vector function. Note that this access bypasses the evaluation counter. Returns:FUNC the objective vector function. Since 3.1

getObjectiveFunction

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protected FUNC getObjectiveFunction()

Gets the objective vector function. Note that this access bypasses the evaluation counter.

Returns:FUNC: the objective vector function.
Since: 3.1

getStartPoint	back to summary
public double[] getStartPoint() Gets the initial values of the optimized parameters. Returns:double[] the initial guess.

getStartPoint

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public double[] getStartPoint()

Gets the initial values of the optimized parameters.

Returns:double[]: the initial guess.

getTarget	back to summary
public double[] getTarget() Gets the observed values to be matched by the objective vector function. Returns:double[] the target values. Since 3.1

getTarget

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public double[] getTarget()

Gets the observed values to be matched by the objective vector function.

Returns:double[]: the target values.
Since: 3.1

getTargetRef	back to summary
protected double[] getTargetRef() Deprecated As of 3.1. Returns:double[] a reference to the `array`. Annotations @Deprecated

getWeight	back to summary
public RealMatrix getWeight() Gets the weight matrix of the observations. Returns:RealMatrix the weight matrix. Since 3.1

getWeight

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public RealMatrix getWeight()

Gets the weight matrix of the observations.

Returns:RealMatrix: the weight matrix.
Since: 3.1

getWeightRef	back to summary
protected double[] getWeightRef() Deprecated As of 3.1. Returns:double[] a reference to the `array`. Annotations @Deprecated

optimize back to summary

optimize	back to summary
public PointVectorValuePair optimize(int maxEval, FUNC f, double[] t, double[] w, double[] startPoint) Implements org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize. Doc from org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize. Deprecated As of 3.1. Please use `optimize(int, MultivariateVectorFunction, OptimizationData[])` instead. Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is `∑weight_i(objective_i - target_i)²` Parameters maxEval:int Maximum number of function evaluations. f:FUNC Objective function. t:double[] Target value for the objective functions at optimum. w:double[] Weights for the least squares cost computation. startPoint:double[] Start point for optimization. Returns:PointVectorValuePair the point/value pair giving the optimal value for objective function. Annotations @Deprecated

public PointVectorValuePair optimize(int maxEval, FUNC f, double[] t, double[] w, double[] startPoint)

Implements org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize.

Doc from org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize.

Deprecated

As of 3.1. Please use optimize(int, MultivariateVectorFunction, OptimizationData[]) instead.

Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is ∑weight_i(objective_i - target_i)²

Parameters

maxEval:int: Maximum number of function evaluations.
f:FUNC: Objective function.
t:double[]: Target value for the objective functions at optimum.
w:double[]: Weights for the least squares cost computation.
startPoint:double[]: Start point for optimization.

Returns:PointVectorValuePair

the point/value pair giving the optimal value for objective function.

Annotations

@Deprecated

optimize back to summary

optimize	back to summary
protected PointVectorValuePair optimize(int maxEval, FUNC f, OptimizationData... optData) throws TooManyEvaluationsException, DimensionMismatchException Deprecated as a consequence of `OptimizationData` and `PointVectorValuePair` being deprecated. See corresponding docs for further information. Optimize an objective function. Parameters maxEval:int Allowed number of evaluations of the objective function. f:FUNC Objective function. optData:OptimizationData[] Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess` Returns:PointVectorValuePair the point/value pair giving the optimal value of the objective function. Exceptions TooManyEvaluationsException: if the maximal number of evaluations is exceeded. DimensionMismatchException: if the initial guess, target, and weight arguments have inconsistent dimensions. Since 3.1

protected PointVectorValuePair optimize(int maxEval, FUNC f, OptimizationData... optData) throws TooManyEvaluationsException, DimensionMismatchException

Deprecated

as a consequence of OptimizationData and PointVectorValuePair being deprecated. See corresponding docs for further information.

Optimize an objective function.

Parameters

maxEval:int

Allowed number of evaluations of the objective function.

f:FUNC

Objective function.

optData:OptimizationData[]

Optimization data. The following data will be looked for:

Returns:PointVectorValuePair

the point/value pair giving the optimal value of the objective function.

Exceptions

TooManyEvaluationsException:: if the maximal number of evaluations is exceeded.
DimensionMismatchException:: if the initial guess, target, and weight arguments have inconsistent dimensions.

Since

3.1

optimizeInternal back to summary

optimizeInternal	back to summary
protected PointVectorValuePair optimizeInternal(final int maxEval, final FUNC f, final double[] t, final double[] w, final double[] startPoint) Deprecated As of 3.1. Please use `optimizeInternal(int, MultivariateVectorFunction, OptimizationData[])` instead. Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is `∑weight_i(objective_i - target_i)²` Parameters maxEval:int Maximum number of function evaluations. f:FUNC Objective function. t:double[] Target value for the objective functions at optimum. w:double[] Weights for the least squares cost computation. startPoint:double[] Start point for optimization. Returns:PointVectorValuePair the point/value pair giving the optimal value for objective function. Annotations @Deprecated Exceptions DimensionMismatchException: if the start point dimension is wrong. TooManyEvaluationsException: if the maximal number of evaluations is exceeded. NullArgumentException: if any argument is `null`.

protected PointVectorValuePair optimizeInternal(final int maxEval, final FUNC f, final double[] t, final double[] w, final double[] startPoint)

Deprecated

As of 3.1. Please use optimizeInternal(int, MultivariateVectorFunction, OptimizationData[]) instead.

Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is ∑weight_i(objective_i - target_i)²

Parameters

maxEval:int: Maximum number of function evaluations.
f:FUNC: Objective function.
t:double[]: Target value for the objective functions at optimum.
w:double[]: Weights for the least squares cost computation.
startPoint:double[]: Start point for optimization.

Returns:PointVectorValuePair

the point/value pair giving the optimal value for objective function.

Annotations

@Deprecated

Exceptions

DimensionMismatchException:: if the start point dimension is wrong.
TooManyEvaluationsException:: if the maximal number of evaluations is exceeded.
NullArgumentException:: if any argument is null.

optimizeInternal back to summary

optimizeInternal	back to summary
protected PointVectorValuePair optimizeInternal(int maxEval, FUNC f, OptimizationData... optData) throws TooManyEvaluationsException, DimensionMismatchException Deprecated as a consequence of `OptimizationData` and `PointVectorValuePair` being deprecated. See corresponding docs for further information. Optimize an objective function. Parameters maxEval:int Allowed number of evaluations of the objective function. f:FUNC Objective function. optData:OptimizationData[] Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess` Returns:PointVectorValuePair the point/value pair giving the optimal value of the objective function. Exceptions TooManyEvaluationsException: if the maximal number of evaluations is exceeded. DimensionMismatchException: if the initial guess, target, and weight arguments have inconsistent dimensions. Since 3.1

protected PointVectorValuePair optimizeInternal(int maxEval, FUNC f, OptimizationData... optData) throws TooManyEvaluationsException, DimensionMismatchException

Deprecated

as a consequence of OptimizationData and PointVectorValuePair being deprecated. See corresponding docs for further information.

Optimize an objective function.

Parameters

maxEval:int

Allowed number of evaluations of the objective function.

f:FUNC

Objective function.

optData:OptimizationData[]

Optimization data. The following data will be looked for:

Returns:PointVectorValuePair

the point/value pair giving the optimal value of the objective function.

Exceptions

TooManyEvaluationsException:: if the maximal number of evaluations is exceeded.
DimensionMismatchException:: if the initial guess, target, and weight arguments have inconsistent dimensions.

Since

3.1

parseOptimizationData back to summary

parseOptimizationData	back to summary
private void parseOptimizationData(OptimizationData... optData) Deprecated as a consequence of `OptimizationData` being deprecated. See corresponding docs for further information. Scans the list of (required and optional) optimization data that characterize the problem. Parameters optData:OptimizationData[] Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess`

private void parseOptimizationData(OptimizationData... optData)

Deprecated

as a consequence of OptimizationData being deprecated. See corresponding docs for further information.

Scans the list of (required and optional) optimization data that characterize the problem.

Parameters

optData:OptimizationData[]

Optimization data. The following data will be looked for:

setUp back to summary

setUp	back to summary
protected void setUp() Method which a subclass must override whenever its internal state depend on the `input` parsed by this base class. It will be called after the parsing step performed in the `optimize` method and just before `doOptimize()`. Since 3.1

protected void setUp()

Method which a subclass must override whenever its internal state depend on the input parsed by this base class. It will be called after the parsing step performed in the optimize method and just before doOptimize().

Since: 3.1

public abstract Class BaseAbstractMultivariateVectorOptimizer<FUNC extends MultivariateVectorFunction>

Field Summary

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Field Detail

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