public class SimplexOptimizer extends 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.
The simplex update procedure (NelderMeadSimplex or MultiDirectionalSimplex) must be passed to the
optimize method.
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 algorithm must be used like CMAESOptimizer or
BOBYQAOptimizer, or the objective function must be wrapped in an adapter like
MultivariateFunctionMappingAdapter or
MultivariateFunctionPenaltyAdapter.
evaluations, iterations| Constructor and Description |
|---|
SimplexOptimizer(ConvergenceChecker<PointValuePair> checker) |
SimplexOptimizer(double rel,
double abs) |
| Modifier and Type | Method and Description |
|---|---|
protected PointValuePair |
doOptimize()
Performs the bulk of the optimization algorithm.
|
PointValuePair |
optimize(OptimizationData... optData)
Stores data and performs the optimization.
|
computeObjectiveValue, getGoalTypegetLowerBound, getStartPoint, getUpperBoundgetConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCountpublic SimplexOptimizer(ConvergenceChecker<PointValuePair> checker)
checker - Convergence checker.public SimplexOptimizer(double rel,
double abs)
rel - Relative threshold.abs - Absolute threshold.public PointValuePair optimize(OptimizationData... optData)
optimize in class MultivariateOptimizeroptData - Optimization data.
The following data will be looked for:
protected PointValuePair doOptimize()
doOptimize in class BaseOptimizer<PointValuePair>Copyright © 2019 CNES. All rights reserved.