## org.apache.commons.math3.optimization
Interface BaseMultivariateVectorOptimizer<FUNC extends MultivariateVectorFunction>

Type Parameters:
  `FUNC` - Type of the objective function to be optimized.

All Superinterfaces:
  BaseOptimizer<PointVectorValuePair>

All Known Subinterfaces:
  DifferentiableMultivariateVectorOptimizer, MultivariateDifferentiableVectorOptimizer

All Known Implementing Classes:
  AbstractLeastSquaresOptimizer, BaseAbstractMultivariateVectorOptimizer, BaseMultivariateVectorMultiStartOptimizer, DifferentiableMultivariateVectorMultiStartOptimizer, GaussNewtonOptimizer, LevenbergMarquardtOptimizer, MultivariateDifferentiableVectorMultiStartOptimizer

Deprecated. As of 3.1 (to be removed in 4.0).

    @Deprecated
    public interface BaseMultivariateVectorOptimizer<FUNC extends MultivariateVectorFunction>

extends BaseOptimizer<PointVectorValuePair>

This interface is mainly intended to enforce the internal coherence of Commons-Math. Users of the API are advised to base their code on the following interfaces:

- `DifferentiableMultivariateVectorOptimizer`

Since:
  3.0
Version:
  $Id: BaseMultivariateVectorOptimizer.java 1422230 2012-12-15 12:11:13Z erans $

Method Summary
`PointVectorValuePair` `optimize(int maxEval, FUNC f, double[] target, double[] weight, double[] startPoint)` Deprecated. As of 3.1. In 4.0, this will be replaced by the declaration corresponding to this `method`.

Methods inherited from interface org.apache.commons.math3.optimization.BaseOptimizer
`getConvergenceChecker, getEvaluations, getMaxEvaluations`

Method Detail

### optimize

    @Deprecated
    PointVectorValuePair optimize(int maxEval,
                                             FUNC f,
                                             double[] target,
                                             double[] weight,
                                             double[] startPoint)

  Deprecated. As of 3.1. In 4.0, this will be replaced by the declaration corresponding to this `method`.

  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`

  Parameters:
    `f` - Objective function.
    `target` - Target value for the objective functions at optimum.
    `weight` - Weights for the least squares cost computation.
    `startPoint` - Start point for optimization.
    `maxEval` - Maximum number of function evaluations.
  Returns:
    the point/value pair giving the optimal value for objective function.
  Throws:
    `DimensionMismatchException` - if the start point dimension is wrong.
    `TooManyEvaluationsException` - if the maximal number of evaluations is exceeded.
    `NullArgumentException` - if any argument is `null`.

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