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 Generally, optimizers are algorithms that will either minimize or maximize a scalar function, called the objective
 function. 
 For some scalar objective functions the gradient can be computed (analytically or numerically). Algorithms that use this knowledge are defined in the gradient package. The algorithms that do not need this additional information are located in the noderiv package. 
 Some problems are solved more efficiently by algorithms that, instead of an objective function, need access to a model function: such a model predicts a set of values which the algorithm tries to match with a set of given target values. Those algorithms are located in the vector package. 
 Algorithms that also require the 
 Jacobian matrix of the model are located in the jacobian package. 
 The 
 non-linear least-squares optimizers are a specialization of the the latter, that minimize the distance (called cost or χ2)
 between model and observations.
 
 For cases where the Jacobian cannot be provided, a utility class will 
 convert a (vector) model into a (scalar) objective function. 
 This package provides common functionality for the optimization algorithms. Abstract classes (BaseOptimizer and BaseMultivariateOptimizer) contain boiler-plate code for storing 
 evaluations and iterations counters and a user-defined convergence checker. 

 For each of the optimizer types, there is a special implementation that
 wraps an optimizer instance and provides a "multi-start" feature: it calls
 the underlying optimizer several times with different starting points and
 returns the best optimum found, or all optima if so desired.
 This could be useful to avoid being trapped in a local extremum.

/**
 * <p>
 *  Generally, optimizers are algorithms that will either
 *  {@link org.apache.commons.math3.optim.nonlinear.scalar.GoalType#MINIMIZE minimize} or
 *  {@link org.apache.commons.math3.optim.nonlinear.scalar.GoalType#MAXIMIZE maximize}
 *  a scalar function, called the
 *  {@link org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction <em>objective
 *  function</em>}.
 *  <br/>
 *  For some scalar objective functions the gradient can be computed (analytically
 *  or numerically). Algorithms that use this knowledge are defined in the
 *  {@link org.apache.commons.math3.optim.nonlinear.scalar.gradient} package.
 *  The algorithms that do not need this additional information are located in
 *  the {@link org.apache.commons.math3.optim.nonlinear.scalar.noderiv} package.
 * </p>
 *
 * <p>
 *  Some problems are solved more efficiently by algorithms that, instead of an
 *  objective function, need access to a
 *  {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunction
 *  <em>model function</em>}: such a model predicts a set of values which the
 *  algorithm tries to match with a set of given
 *  {@link org.apache.commons.math3.optim.nonlinear.vector.Target target values}.
 *  Those algorithms are located in the
 *  {@link org.apache.commons.math3.optim.nonlinear.vector} package.
 *  <br/>
 *  Algorithms that also require the
 *  {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian
 *  Jacobian matrix of the model} are located in the
 *  {@link org.apache.commons.math3.optim.nonlinear.vector.jacobian} package.
 *  <br/>
 *  The {@link org.apache.commons.math3.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer
 *  non-linear least-squares optimizers} are a specialization of the the latter,
 *  that minimize the distance (called <em>cost</em> or <em>&chi;<sup>2</sup></em>)
 *  between model and observations.
 *  <br/>
 *  For cases where the Jacobian cannot be provided, a utility class will
 *  {@link org.apache.commons.math3.optim.nonlinear.scalar.LeastSquaresConverter
 *  convert} a (vector) model into a (scalar) objective function.
 * </p>
 *
 * <p>
 *  This package provides common functionality for the optimization algorithms.
 *  Abstract classes ({@link org.apache.commons.math3.optim.BaseOptimizer} and
 *  {@link org.apache.commons.math3.optim.BaseMultivariateOptimizer}) contain
 *  boiler-plate code for storing {@link org.apache.commons.math3.optim.MaxEval
 *  evaluations} and {@link org.apache.commons.math3.optim.MaxIter iterations}
 *  counters and a user-defined
 *  {@link org.apache.commons.math3.optim.ConvergenceChecker convergence checker}.
 * </p>
 *
 * <p>
 *  For each of the optimizer types, there is a special implementation that
 *  wraps an optimizer instance and provides a "multi-start" feature: it calls
 *  the underlying optimizer several times with different starting points and
 *  returns the best optimum found, or all optima if so desired.
 *  This could be useful to avoid being trapped in a local extremum.
 * </p>
 */
package org.apache.commons.math3.optim;