Apache Commons Math 3.6.1 · org.apache.commons.math3.optimization.general.AbstractLeastSquaresOptimizer

Modifier and Type	Field and Description
protected int	cols Deprecated As of 3.1. Number of columns of the jacobian matrix.
protected double	cost Deprecated As of 3.1. Field to become "private" in 4.0. Please use `setCost(double)`. Cost value (square root of the sum of the residuals).
private static final double	DEFAULT_SINGULARITY_THRESHOLD Deprecated As of 3.1. Singularity threshold (cf. `getCovariances(double)`).
private int	jacobianEvaluations Number of evaluations of the Jacobian.
private MultivariateDifferentiableVectorFunction	jF Objective function derivatives.
protected double[]	objective Deprecated As of 3.1. Current objective function value.
protected double[]	point Deprecated As of 3.1. Current point.
protected int	rows Deprecated As of 3.1. Number of rows of the jacobian matrix.
protected double[][]	weightedResidualJacobian Deprecated As of 3.1. Jacobian matrix of the weighted residuals.
protected double[]	weightedResiduals Deprecated As of 3.1. Weighted residuals
private RealMatrix	weightMatrixSqrt Square-root of the weight matrix.

Access	Constructor and Description
protected	AbstractLeastSquaresOptimizer() Deprecated See `org.apache.commons.math3.optimization.SimpleValueChecker#SimpleValueChecker()` Simple constructor with default settings.
protected	AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> Convergence checker. checker) Deprecated as a consequence of `ConvergenceChecker` being deprecated.

Method Summary

Modifier and Type	Method and Description
protected double	Returns: the cost. computeCost(double[] Residuals. residuals) Computes the cost.
public double[][]	Returns: the covariance matrix. computeCovariances(double[] Model parameters. params, double Singularity threshold. threshold) Get the covariance matrix of the optimized parameters.
protected double[]	Returns: the residuals. computeResiduals(double[] Value of the the objective function. This is the value returned from a call to `computeObjectiveValue` (whose array argument contains the model parameters). objectiveValue) Computes the residuals.
public double[]	Returns: an estimate of the standard deviation of the optimized parameters computeSigma(double[] Model parameters. params, double Singularity threshold (see `computeCovariances`). covarianceSingularityThreshold) Computes an estimate of the standard deviation of the parameters.
protected RealMatrix	Returns: the weighted Jacobian: W^1/2 J. computeWeightedJacobian(double[] Model parameters at which to compute the Jacobian. params) Computes the Jacobian matrix.
public double	Returns: chi-square value getChiSquare() 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.
public double[][]	Returns: the covariance matrix. getCovariances() Deprecated As of 3.1. Please use `computeCovariances(double[], double)` instead. Get the covariance matrix of the optimized parameters.
public double[][]	Returns: the covariance matrix. getCovariances(double Singularity threshold. threshold) Deprecated As of 3.1. Please use `computeCovariances(double[], double)` instead. Get the covariance matrix of the optimized parameters.
public int	Returns: the number of evaluations of the Jacobian function. getJacobianEvaluations()
public double	Returns: RMS value getRMS() Get the Root Mean Square value.
public RealMatrix	Returns: the square-root of the weight matrix. getWeightSquareRoot() Gets the square-root of the weight matrix.
public double[]	Returns: an estimate of the standard deviation of the optimized parameters guessParametersErrors() Deprecated as of version 3.1, `computeSigma(double[], double)` should be used instead. Returns an estimate of the standard deviation of each parameter.
public PointVectorValuePair	optimize(int Maximum number of function evaluations. maxEval, final DifferentiableMultivariateVectorFunction Objective function. f, final double[] Target value for the objective functions at optimum. target, final double[] Weights for the least squares cost computation. weights, final double[] Start point for optimization. startPoint) Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimize. Implements org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize. Deprecated As of 3.1. Optimize an objective function.
public PointVectorValuePair	Returns: the point/value pair giving the optimal value for objective function. optimize(final int Maximum number of function evaluations. maxEval, final MultivariateDifferentiableVectorFunction Objective function. f, final double[] Target value for the objective functions at optimum. target, final double[] Weights for the least squares cost computation. weights, final double[] Start point for optimization. startPoint) Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimize. 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. optimizeInternal(final int Allowed number of evaluations of the objective function. maxEval, final MultivariateDifferentiableVectorFunction Objective function. f, OptimizationData... Optimization data. The following data will be looked for: `Target` `Weight` `InitialGuess` optData) Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimizeInternal. Deprecated As of 3.1. Override is necessary only until this class's generic argument is changed to `MultivariateDifferentiableVectorFunction`. Optimize an objective function.
protected void	setCost(double Cost value. cost) Sets the cost.
protected void	setUp() Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.setUp. Method which a subclass must override whenever its internal state depend on the `input` parsed by this base class.
private RealMatrix	Returns: the square-root of the weight matrix. squareRoot(RealMatrix Symmetric, positive-definite (weight) matrix. m) Computes the square-root of the weight matrix.
protected void	updateJacobian() Deprecated As of 3.1. Please use `computeWeightedJacobian(double[])` instead. Update the jacobian matrix.
protected void	updateResidualsAndCost() Deprecated As of 3.1. Update the residuals array and cost function value.

Inherited from org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer:: computeObjectiveValue doOptimize getConvergenceChecker getEvaluations getMaxEvaluations getObjectiveFunction getStartPoint getTarget getTargetRef getWeight getWeightRef optimize optimizeInternal

cols	back to summary
protected int cols Deprecated As of 3.1. Number of columns of the jacobian matrix. Annotations @Deprecated

cost	back to summary
protected double cost Deprecated As of 3.1. Field to become "private" in 4.0. Please use `setCost(double)`. Cost value (square root of the sum of the residuals). Annotations @Deprecated

DEFAULT_SINGULARITY_THRESHOLD	back to summary
private static final double DEFAULT_SINGULARITY_THRESHOLD Deprecated As of 3.1. Singularity threshold (cf. `getCovariances(double)`). Annotations @Deprecated

jacobianEvaluations	back to summary
private int jacobianEvaluations Number of evaluations of the Jacobian.

jF	back to summary
private MultivariateDifferentiableVectorFunction jF Objective function derivatives.

objective	back to summary
protected double[] objective Deprecated As of 3.1. Current objective function value. Annotations @Deprecated

point	back to summary
protected double[] point Deprecated As of 3.1. Current point. Annotations @Deprecated

rows	back to summary
protected int rows Deprecated As of 3.1. Number of rows of the jacobian matrix. Annotations @Deprecated

weightedResidualJacobian	back to summary
protected double[][] weightedResidualJacobian Deprecated As of 3.1. To be removed in 4.0. Please use `computeWeightedJacobian(double[])` instead. Jacobian matrix of the weighted residuals. This matrix is in canonical form just after the calls to `updateJacobian()`, but may be modified by the solver in the derived class (the `Levenberg-Marquardt optimizer` does this). Annotations @Deprecated

weightedResiduals	back to summary
protected double[] weightedResiduals Deprecated As of 3.1. Weighted residuals Annotations @Deprecated

weightMatrixSqrt	back to summary
private RealMatrix weightMatrixSqrt Square-root of the weight matrix.

AbstractLeastSquaresOptimizer	back to summary
protected AbstractLeastSquaresOptimizer() Deprecated See `org.apache.commons.math3.optimization.SimpleValueChecker#SimpleValueChecker()` Simple constructor with default settings. The convergence check is set to a `org.apache.commons.math3.optimization.SimpleVectorValueChecker`. Annotations @Deprecated

AbstractLeastSquaresOptimizer	back to summary
protected AbstractLeastSquaresOptimizer(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

computeCost back to summary

computeCost	back to summary
protected double computeCost(double[] residuals) Computes the cost. Parameters residuals:double[] Residuals. Returns:double the cost. Since 3.1 See Also `computeResiduals(double[])`

protected double computeCost(double[] residuals)

Computes the cost.

Parameters

residuals:double[]: Residuals.

Returns:double

the cost.

Since

3.1

See Also

computeResiduals(double[])

computeCovariances back to summary

computeCovariances	back to summary
public double[][] computeCovariances(double[] params, double threshold) Get the covariance matrix of the optimized parameters. Note that this operation involves the inversion of the `J^TJ` 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:double[] Model parameters. threshold:double Singularity threshold. Returns:double[][] the covariance matrix. Exceptions SingularMatrixException: if the covariance matrix cannot be computed (singular problem). Since 3.1

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

Get the covariance matrix of the optimized parameters.
Note that this operation involves the inversion of the J^TJ 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:double[]: Model parameters.
threshold:double: Singularity threshold.

Returns:double[][]

the covariance matrix.

Exceptions

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

Since

3.1

computeResiduals back to summary

computeResiduals	back to summary
protected double[] computeResiduals(double[] objectiveValue) Computes the residuals. The residual is the difference between the observed (target) values and the model (objective function) value. There is one residual for each element of the vector-valued function. Parameters objectiveValue:double[] Value of the the objective function. This is the value returned from a call to `computeObjectiveValue` (whose array argument contains the model parameters). Returns:double[] the residuals. Exceptions DimensionMismatchException: if `params` has a wrong length. Since 3.1

protected double[] computeResiduals(double[] objectiveValue)

Computes the residuals. The residual is the difference between the observed (target) values and the model (objective function) value. There is one residual for each element of the vector-valued function.

Parameters

objectiveValue:double[]: Value of the the objective function. This is the value returned from a call to computeObjectiveValue (whose array argument contains the model parameters).

Returns:double[]

the residuals.

Exceptions

DimensionMismatchException:: if params has a wrong length.

Since

3.1

computeSigma back to summary

computeSigma	back to summary
public double[] computeSigma(double[] params, double covarianceSingularityThreshold) 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:double[] Model parameters. covarianceSingularityThreshold:double Singularity threshold (see `computeCovariances`). Returns:double[] an estimate of the standard deviation of the optimized parameters Exceptions SingularMatrixException: if the covariance matrix cannot be computed. Since 3.1

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

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:double[]: Model parameters.
covarianceSingularityThreshold:double: Singularity threshold (see computeCovariances).

Returns:double[]

an estimate of the standard deviation of the optimized parameters

Exceptions

SingularMatrixException:: if the covariance matrix cannot be computed.

Since

3.1

computeWeightedJacobian	back to summary
protected RealMatrix computeWeightedJacobian(double[] params) Computes the Jacobian matrix. Parameters params:double[] Model parameters at which to compute the Jacobian. Returns:RealMatrix the weighted Jacobian: W^1/2 J. Exceptions DimensionMismatchException: if the Jacobian dimension does not match problem dimension. Since 3.1

computeWeightedJacobian

back to summary

protected RealMatrix computeWeightedJacobian(double[] params)

Computes the Jacobian matrix.

Parameters

params:double[]: Model parameters at which to compute the Jacobian.

Returns:RealMatrix

the weighted Jacobian: W^1/2 J.

Exceptions

DimensionMismatchException:: if the Jacobian dimension does not match problem dimension.

Since

3.1

getChiSquare	back to summary
public double getChiSquare() 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:double chi-square value

getChiSquare

back to summary

public double getChiSquare()

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:double: chi-square value

getCovariances back to summary

getCovariances	back to summary
public double[][] getCovariances() Deprecated As of 3.1. Please use `computeCovariances(double[], double)` instead. Get the covariance matrix of the optimized parameters. Returns:double[][] the covariance matrix. Annotations @Deprecated Exceptions SingularMatrixException: if the covariance matrix cannot be computed (singular problem). See Also `getCovariances(double)`

public double[][] getCovariances()

Deprecated

As of 3.1. Please use computeCovariances(double[], double) instead.

Get the covariance matrix of the optimized parameters.

Returns:double[][]

the covariance matrix.

Annotations

@Deprecated

Exceptions

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

See Also

getCovariances(double)

getCovariances back to summary

getCovariances	back to summary
public double[][] getCovariances(double threshold) Deprecated As of 3.1. Please use `computeCovariances(double[], double)` instead. Get the covariance matrix of the optimized parameters. Note that this operation involves the inversion of the `J^TJ` 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 threshold:double Singularity threshold. Returns:double[][] the covariance matrix. Annotations @Deprecated Exceptions SingularMatrixException: if the covariance matrix cannot be computed (singular problem).

public double[][] getCovariances(double threshold)

Deprecated

As of 3.1. Please use computeCovariances(double[], double) instead.

Parameters

threshold:double: Singularity threshold.

Returns:double[][]

the covariance matrix.

Annotations

@Deprecated

Exceptions

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

getJacobianEvaluations	back to summary
public int getJacobianEvaluations() Returns:int the number of evaluations of the Jacobian function.

getRMS	back to summary
public double getRMS() Get the Root Mean Square value. Get the Root Mean Square value, i.e. 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:double RMS value

getRMS

back to summary

public double getRMS()

Get the Root Mean Square value. Get the Root Mean Square value, i.e. 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:double: RMS value

getWeightSquareRoot	back to summary
public RealMatrix getWeightSquareRoot() Gets the square-root of the weight matrix. Returns:RealMatrix the square-root of the weight matrix. Since 3.1

getWeightSquareRoot

back to summary

public RealMatrix getWeightSquareRoot()

Gets the square-root of the weight matrix.

Returns:RealMatrix: the square-root of the weight matrix.
Since: 3.1

guessParametersErrors back to summary

guessParametersErrors	back to summary
public double[] guessParametersErrors() Deprecated as of version 3.1, `computeSigma(double[], double)` should be used instead. It should be emphasized that `guessParametersErrors` and `computeSigma` are not strictly equivalent. Returns an estimate of the standard deviation of each parameter. The returned values are the so-called (asymptotic) standard errors on the parameters, defined as `sd(a[i]) = sqrt(S / (n - m) * C[i][i])`, where `a[i]` is the optimized value of the `i`-th parameter, `S` is the minimized value of the sum of squares objective function (as returned by `getChiSquare()`), `n` is the number of observations, `m` is the number of parameters and `C` is the covariance matrix. See also Wikipedia, or MathWorld, equations (34) and (35) for a particular case. Returns:double[] an estimate of the standard deviation of the optimized parameters Annotations @Deprecated Exceptions SingularMatrixException: if the covariance matrix cannot be computed. NumberIsTooSmallException: if the number of degrees of freedom is not positive, i.e. the number of measurements is less or equal to the number of parameters.

public double[] guessParametersErrors()

Deprecated

as of version 3.1, computeSigma(double[], double) should be used instead. It should be emphasized that guessParametersErrors and computeSigma are not strictly equivalent.

Returns an estimate of the standard deviation of each parameter. The returned values are the so-called (asymptotic) standard errors on the parameters, defined as sd(a[i]) = sqrt(S / (n - m) * C[i][i]), where a[i] is the optimized value of the i-th parameter, S is the minimized value of the sum of squares objective function (as returned by getChiSquare()), n is the number of observations, m is the number of parameters and C is the covariance matrix.

See also Wikipedia, or MathWorld, equations (34) and (35) for a particular case.

Returns:double[]

an estimate of the standard deviation of the optimized parameters

Annotations

@Deprecated

Exceptions

SingularMatrixException:: if the covariance matrix cannot be computed.
NumberIsTooSmallException:: if the number of degrees of freedom is not positive, i.e. the number of measurements is less or equal to the number of parameters.

optimize back to summary

optimize	back to summary
public PointVectorValuePair optimize(int maxEval, final DifferentiableMultivariateVectorFunction f, final double[] target, final double[] weights, final double[] startPoint) Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimize. 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,MultivariateDifferentiableVectorFunction,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:DifferentiableMultivariateVectorFunction Objective function. target:double[] Target value for the objective functions at optimum. weights: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 @Override @Deprecated

public PointVectorValuePair optimize(int maxEval, final DifferentiableMultivariateVectorFunction f, final double[] target, final double[] weights, final double[] startPoint)

Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimize.

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,MultivariateDifferentiableVectorFunction,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:DifferentiableMultivariateVectorFunction: Objective function.
target:double[]: Target value for the objective functions at optimum.
weights: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

@Override
@Deprecated

optimize back to summary

optimize	back to summary
public PointVectorValuePair optimize(final int maxEval, final MultivariateDifferentiableVectorFunction f, final double[] target, final double[] weights, final double[] startPoint) Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimize. Implements org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer.optimize. Deprecated As of 3.1. Please use `optimize(int,MultivariateDifferentiableVectorFunction,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:MultivariateDifferentiableVectorFunction Objective function. target:double[] Target value for the objective functions at optimum. weights: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`.

public PointVectorValuePair optimize(final int maxEval, final MultivariateDifferentiableVectorFunction f, final double[] target, final double[] weights, final double[] startPoint)

Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimize.

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

Deprecated

As of 3.1. Please use optimize(int,MultivariateDifferentiableVectorFunction,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:MultivariateDifferentiableVectorFunction: Objective function.
target:double[]: Target value for the objective functions at optimum.
weights: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(final int maxEval, final MultivariateDifferentiableVectorFunction f, OptimizationData... optData) Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimizeInternal. Deprecated As of 3.1. Override is necessary only until this class's generic argument is changed to `MultivariateDifferentiableVectorFunction`. 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 Allowed number of evaluations of the objective function. f:MultivariateDifferentiableVectorFunction 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. Annotations @Deprecated Exceptions TooManyEvaluationsException: if the maximal number of evaluations is exceeded. DimensionMismatchException: if the target, and weight arguments have inconsistent dimensions. Since 3.1 See Also `BaseAbstractMultivariateVectorOptimizer#optimizeInternal(int, org.apache.commons.math3.analysis.MultivariateVectorFunction, OptimizationData[])`

protected PointVectorValuePair optimizeInternal(final int maxEval, final MultivariateDifferentiableVectorFunction f, OptimizationData... optData)

Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.optimizeInternal.

Deprecated

As of 3.1. Override is necessary only until this class's generic argument is changed to MultivariateDifferentiableVectorFunction.

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

Allowed number of evaluations of the objective function.

f:MultivariateDifferentiableVectorFunction

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.

Annotations

@Deprecated

Exceptions

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

Since

3.1

See Also

BaseAbstractMultivariateVectorOptimizer#optimizeInternal(int, org.apache.commons.math3.analysis.MultivariateVectorFunction, OptimizationData[])

setCost	back to summary
protected void setCost(double cost) Sets the cost. Parameters cost:double Cost value. Since 3.1

setCost

back to summary

protected void setCost(double cost)

Sets the cost.

Parameters

cost:double: Cost value.

Since

3.1

setUp back to summary

setUp	back to summary
protected void setUp() Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.setUp. Doc from org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.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()`. Annotations @Override

protected void setUp()

Overrides org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.setUp.

Doc from org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateVectorOptimizer.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().

Annotations

@Override

squareRoot	back to summary
private RealMatrix squareRoot(RealMatrix m) Computes the square-root of the weight matrix. Parameters m:RealMatrix Symmetric, positive-definite (weight) matrix. Returns:RealMatrix the square-root of the weight matrix.

squareRoot

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private RealMatrix squareRoot(RealMatrix m)

Computes the square-root of the weight matrix.

Parameters

m:RealMatrix: Symmetric, positive-definite (weight) matrix.

Returns:RealMatrix

the square-root of the weight matrix.

updateJacobian back to summary

updateJacobian	back to summary
protected void updateJacobian() Deprecated As of 3.1. Please use `computeWeightedJacobian(double[])` instead. Update the jacobian matrix. Annotations @Deprecated Exceptions DimensionMismatchException: if the Jacobian dimension does not match problem dimension.

protected void updateJacobian()

Deprecated

As of 3.1. Please use computeWeightedJacobian(double[]) instead.

Update the jacobian matrix.

Annotations

@Deprecated

Exceptions

DimensionMismatchException:: if the Jacobian dimension does not match problem dimension.

updateResidualsAndCost back to summary

updateResidualsAndCost	back to summary
protected void updateResidualsAndCost() Deprecated As of 3.1. Please use `computeResiduals(double[])`, `computeObjectiveValue(double[])`, `computeCost(double[])` and `setCost(double)` instead. Update the residuals array and cost function value. Annotations @Deprecated Exceptions DimensionMismatchException: if the dimension does not match the problem dimension. TooManyEvaluationsException: if the maximal number of evaluations is exceeded.

protected void updateResidualsAndCost()

Deprecated

As of 3.1. Please use computeResiduals(double[]), computeObjectiveValue(double[]), computeCost(double[]) and setCost(double) instead.

Update the residuals array and cost function value.

Annotations

@Deprecated

Exceptions

DimensionMismatchException:: if the dimension does not match the problem dimension.
TooManyEvaluationsException:: if the maximal number of evaluations is exceeded.

public abstract Class AbstractLeastSquaresOptimizer

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail