@Deprecated public class SimplexOptimizer extends BaseAbstractMultivariateOptimizer<MultivariateFunction> implements MultivariateOptimizer
Direct search methods only use objective function values, they do not 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.
Simplex-based 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.
setSimplex method must
be called prior to calling the
Each call to
optimize will re-use the start configuration of the current simplex 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 simplex must be
re-initialized to one with the appropriate dimensions.
Convergence is checked by providing the worst points of previous and current simplex to the convergence checker, not the best ones.
This simplex optimizer implementation does not directly support constrained
optimization with simple bounds, so for such optimizations, either a more
dedicated method must be used like
BOBYQAOptimizer, or the optimized method must be wrapped in an adapter like
|Constructor and Description|
|Modifier and Type||Method and Description|
Perform the bulk of the optimization algorithm.
Optimize an objective function.
As of 3.1. The initial simplex can now be passed as an argument of the
computeObjectiveValue, getConvergenceChecker, getEvaluations, getGoalType, getLowerBound, getMaxEvaluations, getStartPoint, getUpperBound, optimize, optimize, optimizeInternal
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
@Deprecated public SimplexOptimizer()
public SimplexOptimizer(ConvergenceChecker<PointValuePair> checker)
checker- Convergence checker.
public SimplexOptimizer(double rel, double abs)
rel- Relative threshold.
abs- Absolute threshold.
@Deprecated public void setSimplex(AbstractSimplex simplex)
protected PointValuePair optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType, OptimizationData... optData)
maxEval- Allowed number of evaluations of the objective function.
f- Objective function.
goalType- Optimization type.
optData- Optimization data. The following data will be looked for:
Copyright © 2003–2015 The Apache Software Foundation. All rights reserved.