MultiStartUnivariateOptimizer (Apache Commons Math 3.3 API)

org.apache.commons.math3.optim.univariate

## Class MultiStartUnivariateOptimizer

- java.lang.Object
- - org.apache.commons.math3.optim.BaseOptimizer<UnivariatePointValuePair>
  - - org.apache.commons.math3.optim.univariate.UnivariateOptimizer
    - - org.apache.commons.math3.optim.univariate.MultiStartUnivariateOptimizer

-     public class MultiStartUnivariateOptimizer
      extends UnivariateOptimizer

  Special implementation of the `UnivariateOptimizer` interface adding multi-start features to an existing optimizer.
  This class wraps an optimizer in order to use it several times in turn with different starting points (trying to avoid being trapped in a local extremum when looking for a global one).
  Since:
    3.0
  Version:
    $Id: MultiStartUnivariateOptimizer.java 1435539 2013-01-19 13:27:24Z tn $

- - ### Field Summary

    - ### Fields inherited from class org.apache.commons.math3.optim.BaseOptimizer

      `evaluations, iterations`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `MultiStartUnivariateOptimizer(UnivariateOptimizer optimizer, int starts, RandomGenerator generator)` Create a multi-start optimizer from a single-start optimizer.

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `protected UnivariatePointValuePair` | `doOptimize()` Performs the bulk of the optimization algorithm.
    `int` | `getEvaluations()` Gets the number of evaluations of the objective function.
    `UnivariatePointValuePair[]` | `getOptima()` Gets all the optima found during the last call to `optimize`.
    `UnivariatePointValuePair` | `optimize(OptimizationData... optData)` Stores data and performs the optimization.

    - ### Methods inherited from class org.apache.commons.math3.optim.univariate.UnivariateOptimizer

      `computeObjectiveValue, getGoalType, getMax, getMin, getStartValue, parseOptimizationData`

    - ### Methods inherited from class org.apache.commons.math3.optim.BaseOptimizer

      `getConvergenceChecker, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount, optimize`

    - ### Methods inherited from class java.lang.Object

      `clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Constructor Detail

    - #### MultiStartUnivariateOptimizer

          public MultiStartUnivariateOptimizer(UnivariateOptimizer 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. If `starts == 1`, the `optimize` methods will return the same solution as `optimizer` would.
        `generator` - Random generator to use for restarts.
      Throws:
        `NotStrictlyPositiveException` - if `starts < 1`.

  - ### Method Detail

    - #### getEvaluations

          public int getEvaluations()

      Gets 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.
      Overrides:
        `getEvaluations` in class `BaseOptimizer<UnivariatePointValuePair>`
      Returns:
        the number of evaluations of the objective function.

    - #### getOptima

          public UnivariatePointValuePair[] getOptima()

      Gets 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 `null` elements corresponding to the runs that did not converge. This means all elements will be `null` if the `optimize` method did throw an exception. This also means that if the first element is not `null`, it is the best point found across all starts.
      Returns:
        an array containing the optima.
      Throws:
        `MathIllegalStateException` - if `optimize` has not been called.

    - #### optimize

          public UnivariatePointValuePair optimize(OptimizationData... optData)

      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.

      Overrides:
        `optimize` in class `UnivariateOptimizer`
      Parameters:
        `optData` - Optimization data. In addition to those documented in `BaseOptimizer`, this method will register the following data:
        - `GoalType`
        - `SearchInterval`
        - `UnivariateObjectiveFunction`
      Returns:
        a point/value pair that satisfies the convergence criteria.
      Throws:
        `MathIllegalStateException` - if `optData` does not contain an instance of `MaxEval` or `SearchInterval`.

    - #### doOptimize

          protected UnivariatePointValuePair doOptimize()

      Performs the bulk of the optimization algorithm.
      Specified by:
        `doOptimize` in class `BaseOptimizer<UnivariatePointValuePair>`
      Returns:
        the point/value pair giving the optimal value of the objective function.

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