public abstract class DirectSearchOptimizer extends java.lang.Object implements MultivariateRealOptimizer
Direct search methods only use objective function values, they don't need derivatives and don't either try to compute approximation of the derivatives. According to a 1996 paper by Margaret H. Wright (Direct Search Methods: Once Scorned, Now Respectable), they are used when either the computation of the derivative is impossible (noisy functions, unpredictable discontinuities) or difficult (complexity, computation cost). In the first cases, rather than an optimum, a not too bad point is desired. In the latter cases, an optimum is desired but cannot be reasonably found. In all cases direct search methods can be useful.
Simplexbased direct search methods are based on comparison of the objective function values at the vertices of a simplex (which is a set of n+1 points in dimension n) that is updated by the algorithms steps.
The initial configuration of the simplex can be set using either
setStartConfiguration(double[])
or setStartConfiguration(double[][])
. If neither method has been called
before optimization is attempted, an explicit call to the first method
with all steps set to +1 is triggered, thus building a default
configuration from a unit hypercube. Each call to optimize
will reuse
the current start configuration and move it such that its first vertex
is at the provided start point of the optimization. If the optimize
method is called to solve a different problem and the number of parameters
change, the start configuration will be reset to a default one with the
appropriate dimensions.
If setConvergenceChecker(RealConvergenceChecker)
is not called,
a default SimpleScalarValueChecker
is used.
Convergence is checked by providing the worst points of previous and current simplex to the convergence checker, not the best ones.
This class is the base class performing the boilerplate simplex initialization and handling. The simplex update by itself is performed by the derived classes according to the implemented algorithms.
implements MultivariateRealOptimizer since 2.0MultivariateRealFunction
,
NelderMead
,
MultiDirectional
Modifier and Type  Method and Description 

RealConvergenceChecker 
getConvergenceChecker()
Get the convergence checker.

int 
getEvaluations()
Get the number of evaluations of the objective function.

int 
getIterations()
Get the number of iterations realized by the algorithm.

int 
getMaxEvaluations()
Get the maximal number of functions evaluations.

int 
getMaxIterations()
Get the maximal number of iterations of the algorithm.

RealPointValuePair 
optimize(MultivariateRealFunction function,
GoalType goalType,
double[] startPoint)
Optimizes an objective function.

void 
setConvergenceChecker(RealConvergenceChecker convergenceChecker)
Set the convergence checker.

void 
setMaxEvaluations(int maxEvaluations)
Set the maximal number of functions evaluations.

void 
setMaxIterations(int maxIterations)
Set the maximal number of iterations of the algorithm.

void 
setStartConfiguration(double[] steps)
Set start configuration for simplex.

void 
setStartConfiguration(double[][] referenceSimplex)
Set start configuration for simplex.

public void setStartConfiguration(double[] steps) throws java.lang.IllegalArgumentException
The start configuration for simplex is built from a box parallel to the canonical axes of the space. The simplex is the subset of vertices of a box parallel to the canonical axes. It is built as the path followed while traveling from one vertex of the box to the diagonally opposite vertex moving only along the box edges. The first vertex of the box will be located at the start point of the optimization.
As an example, in dimension 3 a simplex has 4 vertices. Setting the steps to (1, 10, 2) and the start point to (1, 1, 1) would imply the start simplex would be: { (1, 1, 1), (2, 1, 1), (2, 11, 1), (2, 11, 3) }. The first vertex would be set to the start point at (1, 1, 1) and the last vertex would be set to the diagonally opposite vertex at (2, 11, 3).
steps
 steps along the canonical axes representing box edges,
they may be negative but not nulljava.lang.IllegalArgumentException
 if one step is nullpublic void setStartConfiguration(double[][] referenceSimplex) throws java.lang.IllegalArgumentException
The real initial simplex will be set up by moving the reference simplex such that its first point is located at the start point of the optimization.
referenceSimplex
 reference simplexjava.lang.IllegalArgumentException
 if the reference simplex does not
contain at least one point, or if there is a dimension mismatch
in the reference simplex or if one of its vertices is duplicatedpublic void setMaxIterations(int maxIterations)
setMaxIterations
in interface MultivariateRealOptimizer
maxIterations
 maximal number of algorithm iterationspublic int getMaxIterations()
getMaxIterations
in interface MultivariateRealOptimizer
public void setMaxEvaluations(int maxEvaluations)
setMaxEvaluations
in interface MultivariateRealOptimizer
maxEvaluations
 maximal number of function evaluationspublic int getMaxEvaluations()
getMaxEvaluations
in interface MultivariateRealOptimizer
public int getIterations()
The number of evaluations corresponds to the last call to the
optimize
method. It is 0 if the method has not been called yet.
getIterations
in interface MultivariateRealOptimizer
public int getEvaluations()
The number of evaluations corresponds to the last call to the
optimize
method. It is 0 if the method has not been called yet.
getEvaluations
in interface MultivariateRealOptimizer
public void setConvergenceChecker(RealConvergenceChecker convergenceChecker)
setConvergenceChecker
in interface MultivariateRealOptimizer
convergenceChecker
 object to use to check for convergencepublic RealConvergenceChecker getConvergenceChecker()
getConvergenceChecker
in interface MultivariateRealOptimizer
public RealPointValuePair optimize(MultivariateRealFunction function, GoalType goalType, double[] startPoint) throws FunctionEvaluationException, OptimizationException, java.lang.IllegalArgumentException
optimize
in interface MultivariateRealOptimizer
function
 objective functiongoalType
 type of optimization goal: either GoalType.MAXIMIZE
or GoalType.MINIMIZE
startPoint
 the start point for optimizationFunctionEvaluationException
 if the objective function throws one during
the searchOptimizationException
 if the algorithm failed to convergejava.lang.IllegalArgumentException
 if the start point dimension is wrong"Copyright © 2010  2020 Adobe Systems Incorporated. All Rights Reserved"