Package org.apache.commons.math3.optimization

Class BaseMultivariateVectorMultiStartOptimizer<FUNC extends MultivariateVectorFunction>

java.lang.Object
org.apache.commons.math3.optimization.BaseMultivariateVectorMultiStartOptimizer<FUNC>
Type Parameters:
FUNC - Type of the objective function to be optimized.
All Implemented Interfaces:
BaseMultivariateVectorOptimizer<FUNC>, BaseOptimizer<PointVectorValuePair>
Direct Known Subclasses:
DifferentiableMultivariateVectorMultiStartOptimizer, MultivariateDifferentiableVectorMultiStartOptimizer
@Deprecated public class BaseMultivariateVectorMultiStartOptimizer<FUNC extends MultivariateVectorFunction> extends Object implements BaseMultivariateVectorOptimizer<FUNC>
Deprecated.
As of 3.1 (to be removed in 4.0).
Base class for all implementations of a multi-start optimizer. This interface is mainly intended to enforce the internal coherence of Commons-Math. Users of the API are advised to base their code on DifferentiableMultivariateVectorMultiStartOptimizer.
Since:
3.0

  • Constructor Details

    • BaseMultivariateVectorMultiStartOptimizer

      protected BaseMultivariateVectorMultiStartOptimizer(BaseMultivariateVectorOptimizer<FUNC> optimizer, int starts, RandomVectorGenerator generator)
      Deprecated.
      Create a multi-start optimizer from a single-start optimizer.
      Parameters:
      optimizer - Single-start optimizer to wrap.
      starts - Number of starts to perform. If starts == 1, the optimize will return the same solution as optimizer would.
      generator - Random vector generator to use for restarts.
      Throws:
      NullArgumentException - if optimizer or generator is null.
      NotStrictlyPositiveException - if starts < 1.

  • Method Details

    • getOptima

      public PointVectorValuePair[] getOptima()
      Deprecated.
      Get all the optima found during the last call to optimize. The optimizer stores all the optima found during a set of restarts. The optimize method returns the best point only. This method returns all the points found at the end of each starts, including the best one already returned by the optimize method.
      The returned array as one element for each start as specified in the constructor. It is ordered with the results from the runs that did converge first, sorted from best to worst objective value (i.e. in ascending order if minimizing and in descending order if maximizing), followed by and null elements corresponding to the runs that did not converge. This means all elements will be null if the optimize method did throw a ConvergenceException). This also means that if the first element is not null, it is the best point found across all starts.
      Returns:
      array containing the optima
      Throws:
      MathIllegalStateException - if optimize has not been called.

    • getMaxEvaluations

      public int getMaxEvaluations()
      Deprecated.
      Get the maximal number of function evaluations.
      Specified by:
      getMaxEvaluations in interface BaseOptimizer<FUNC extends MultivariateVectorFunction>
      Returns:
      the maximal number of function evaluations.

    • getEvaluations

      public int getEvaluations()
      Deprecated.
      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.
      Specified by:
      getEvaluations in interface BaseOptimizer<FUNC extends MultivariateVectorFunction>
      Returns:
      the number of evaluations of the objective function.

    • getConvergenceChecker

      public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker()
      Deprecated.
      Get the convergence checker.
      Specified by:
      getConvergenceChecker in interface BaseOptimizer<FUNC extends MultivariateVectorFunction>
      Returns:
      the object used to check for convergence.

    • optimize

      public PointVectorValuePair optimize(int maxEval, FUNC f, double[] target, double[] weights, double[] startPoint)
      Deprecated.
      Optimize an objective function. Optimization is considered to be a weighted least-squares minimization. The cost function to be minimized is ∑weighti(objectivei - targeti)2
      Specified by:
      optimize in interface BaseMultivariateVectorOptimizer<FUNC extends MultivariateVectorFunction>
      Parameters:
      maxEval - Maximum number of function evaluations.
      f - Objective function.
      target - Target value for the objective functions at optimum.
      weights - Weights for the least squares cost computation.
      startPoint - Start point for optimization.
      Returns:
      the point/value pair giving the optimal value for objective function.