Class MultiStartUnivariateRealOptimizer

java.lang.Object
org.apache.commons.math.optimization.MultiStartUnivariateRealOptimizer
All Implemented Interfaces:
ConvergingAlgorithm, UnivariateRealOptimizer

public class MultiStartUnivariateRealOptimizer extends Object implements UnivariateRealOptimizer
Special implementation of the UnivariateRealOptimizer interface adding multi-start features to an existing optimizer.

This class wraps a classical optimizer to use it several times in turn with different starting points in order to avoid being trapped into a local extremum when looking for a global one.

Since:
2.0
Version:
$Revision: 1070725 $ $Date: 2011-02-15 02:31:12 +0100 (mar. 15 févr. 2011) $
  • Constructor Details

    • MultiStartUnivariateRealOptimizer

      public MultiStartUnivariateRealOptimizer(UnivariateRealOptimizer optimizer, int starts, RandomGenerator generator)
      Create a multi-start optimizer from a single-start optimizer
      Parameters:
      optimizer - single-start optimizer to wrap
      starts - number of starts to perform (including the first one), multi-start is disabled if value is less than or equal to 1
      generator - random generator to use for restarts
  • Method Details

    • getFunctionValue

      public double getFunctionValue()
      Get the result of the last run of the optimizer.
      Specified by:
      getFunctionValue in interface UnivariateRealOptimizer
      Returns:
      the value of the function at the optimum.
    • getResult

      public double getResult()
      Get the result of the last run of the optimizer.
      Specified by:
      getResult in interface UnivariateRealOptimizer
      Returns:
      the optimum.
    • getAbsoluteAccuracy

      public double getAbsoluteAccuracy()
      Get the actual absolute accuracy.
      Specified by:
      getAbsoluteAccuracy in interface ConvergingAlgorithm
      Returns:
      the accuracy
    • getIterationCount

      public int getIterationCount()
      Get the number of iterations in the last run of the algorithm.

      This is mainly meant for testing purposes. It may occasionally help track down performance problems: if the iteration count is notoriously high, check whether the problem is evaluated properly, and whether another algorithm is more amenable to the problem.

      Specified by:
      getIterationCount in interface ConvergingAlgorithm
      Returns:
      the last iteration count.
    • getMaximalIterationCount

      public int getMaximalIterationCount()
      Get the upper limit for the number of iterations.
      Specified by:
      getMaximalIterationCount in interface ConvergingAlgorithm
      Returns:
      the actual upper limit
    • getMaxEvaluations

      public int getMaxEvaluations()
      Get the maximal number of functions evaluations.
      Specified by:
      getMaxEvaluations in interface UnivariateRealOptimizer
      Returns:
      the maximal number of functions evaluations.
    • getEvaluations

      public int 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.

      Specified by:
      getEvaluations in interface UnivariateRealOptimizer
      Returns:
      the number of evaluations of the objective function.
    • getRelativeAccuracy

      public double getRelativeAccuracy()
      Get the actual relative accuracy.
      Specified by:
      getRelativeAccuracy in interface ConvergingAlgorithm
      Returns:
      the accuracy
    • resetAbsoluteAccuracy

      public void resetAbsoluteAccuracy()
      Reset the absolute accuracy to the default.

      The default value is provided by the algorithm implementation.

      Specified by:
      resetAbsoluteAccuracy in interface ConvergingAlgorithm
    • resetMaximalIterationCount

      public void resetMaximalIterationCount()
      Reset the upper limit for the number of iterations to the default.

      The default value is supplied by the algorithm implementation.

      Specified by:
      resetMaximalIterationCount in interface ConvergingAlgorithm
      See Also:
    • resetRelativeAccuracy

      public void resetRelativeAccuracy()
      Reset the relative accuracy to the default. The default value is provided by the algorithm implementation.
      Specified by:
      resetRelativeAccuracy in interface ConvergingAlgorithm
    • setAbsoluteAccuracy

      public void setAbsoluteAccuracy(double accuracy)
      Set the absolute accuracy.

      The default is usually chosen so that results in the interval -10..-0.1 and +0.1..+10 can be found with a reasonable accuracy. If the expected absolute value of your results is of much smaller magnitude, set this to a smaller value.

      Algorithms are advised to do a plausibility check with the relative accuracy, but clients should not rely on this.

      Specified by:
      setAbsoluteAccuracy in interface ConvergingAlgorithm
      Parameters:
      accuracy - the accuracy.
    • setMaximalIterationCount

      public void setMaximalIterationCount(int count)
      Set the upper limit for the number of iterations.

      Usually a high iteration count indicates convergence problems. However, the "reasonable value" varies widely for different algorithms. Users are advised to use the default value supplied by the algorithm.

      A ConvergenceException will be thrown if this number is exceeded.

      Specified by:
      setMaximalIterationCount in interface ConvergingAlgorithm
      Parameters:
      count - maximum number of iterations
    • setMaxEvaluations

      public void setMaxEvaluations(int maxEvaluations)
      Set the maximal number of functions evaluations.
      Specified by:
      setMaxEvaluations in interface UnivariateRealOptimizer
      Parameters:
      maxEvaluations - maximal number of function evaluations
    • setRelativeAccuracy

      public void setRelativeAccuracy(double accuracy)
      Set the relative accuracy.

      This is used to stop iterations if the absolute accuracy can't be achieved due to large values or short mantissa length.

      If this should be the primary criterion for convergence rather then a safety measure, set the absolute accuracy to a ridiculously small value, like MathUtils.SAFE_MIN.

      Specified by:
      setRelativeAccuracy in interface ConvergingAlgorithm
      Parameters:
      accuracy - the relative accuracy.
    • getOptima

      public double[] getOptima() throws IllegalStateException
      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 Double.NaN elements corresponding to the runs that did not converge. This means all elements will be NaN if the optimize method did throw a ConvergenceException). This also means that if the first element is not NaN, it is the best point found across all starts.

      Returns:
      array containing the optima
      Throws:
      IllegalStateException - if optimize has not been called
      See Also:
    • getOptimaValues

      public double[] getOptimaValues() throws IllegalStateException
      Get all the function values at optima found during the last call to optimize.

      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 Double.NaN elements corresponding to the runs that did not converge. This means all elements will be NaN if the optimize method did throw a ConvergenceException). This also means that if the first element is not NaN, it is the best point found across all starts.

      Returns:
      array containing the optima
      Throws:
      IllegalStateException - if optimize has not been called
      See Also:
    • optimize

      public double optimize(UnivariateRealFunction f, GoalType goalType, double min, double max) throws ConvergenceException, FunctionEvaluationException
      Find an optimum in the given interval.

      An optimizer may require that the interval brackets a single optimum.

      Specified by:
      optimize in interface UnivariateRealOptimizer
      Parameters:
      f - the function to optimize.
      goalType - type of optimization goal: either GoalType.MAXIMIZE or GoalType.MINIMIZE.
      min - the lower bound for the interval.
      max - the upper bound for the interval.
      Returns:
      a value where the function is optimum.
      Throws:
      ConvergenceException - if the maximum iteration count is exceeded or the optimizer detects convergence problems otherwise.
      FunctionEvaluationException - if an error occurs evaluating the function.
    • optimize

      public double optimize(UnivariateRealFunction f, GoalType goalType, double min, double max, double startValue) throws ConvergenceException, FunctionEvaluationException
      Find an optimum in the given interval, start at startValue.

      An optimizer may require that the interval brackets a single optimum.

      Specified by:
      optimize in interface UnivariateRealOptimizer
      Parameters:
      f - the function to optimize.
      goalType - type of optimization goal: either GoalType.MAXIMIZE or GoalType.MINIMIZE.
      min - the lower bound for the interval.
      max - the upper bound for the interval.
      startValue - the start value to use.
      Returns:
      a value where the function is optimum.
      Throws:
      ConvergenceException - if the maximum iteration count is exceeded or the optimizer detects convergence problems otherwise.
      FunctionEvaluationException - if an error occurs evaluating the function.