Class JacobianMultivariateVectorOptimizer

  extended by org.apache.commons.math3.optim.BaseOptimizer<PAIR>
      extended by org.apache.commons.math3.optim.BaseMultivariateOptimizer<PointVectorValuePair>
          extended by org.apache.commons.math3.optim.nonlinear.vector.MultivariateVectorOptimizer
              extended by org.apache.commons.math3.optim.nonlinear.vector.JacobianMultivariateVectorOptimizer
Direct Known Subclasses:

public abstract class JacobianMultivariateVectorOptimizer
extends MultivariateVectorOptimizer

Base class for implementing optimizers for multivariate vector differentiable functions. It contains boiler-plate code for dealing with Jacobian evaluation. It assumes that the rows of the Jacobian matrix iterate on the model functions while the columns iterate on the parameters; thus, the numbers of rows is equal to the dimension of the Target while the number of columns is equal to the dimension of the InitialGuess.

$Id: 1454464 2013-03-08 16:58:10Z luc $

Field Summary
Fields inherited from class org.apache.commons.math3.optim.BaseOptimizer
evaluations, iterations
Constructor Summary
protected JacobianMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker)
Method Summary
protected  double[][] computeJacobian(double[] params)
          Computes the Jacobian matrix.
 PointVectorValuePair optimize(OptimizationData... optData)
          Stores data and performs the optimization.
protected  void parseOptimizationData(OptimizationData... optData)
          Scans the list of (required and optional) optimization data that characterize the problem.
Methods inherited from class org.apache.commons.math3.optim.nonlinear.vector.MultivariateVectorOptimizer
computeObjectiveValue, 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
doOptimize, getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail


protected JacobianMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker)
checker - Convergence checker.
Method Detail


protected double[][] computeJacobian(double[] params)
Computes the Jacobian matrix.

params - Point at which the Jacobian must be evaluated.
the Jacobian at the specified point.


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

optimize in class MultivariateVectorOptimizer
optData - Optimization data. In addition to those documented in MultivariateVectorOptimizer.optimize(OptimizationData...) MultivariateOptimizer}, this method will register the following data:
a point/value pair that satifies the convergence criteria.
TooManyEvaluationsException - if the maximal number of evaluations is exceeded.
DimensionMismatchException - if the initial guess, target, and weight arguments have inconsistent dimensions.


protected void parseOptimizationData(OptimizationData... optData)
Scans the list of (required and optional) optimization data that characterize the problem.

parseOptimizationData in class MultivariateVectorOptimizer
optData - Optimization data. The following data will be looked for:

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