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.
The call to optimize will throw
MathRuntimeException if bounds are passed to it.
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.
|
protected void |
parseOptimizationData(OptimizationData... optData)
Scans the list of (required and optional) optimization data that
characterize the problem.
|
computeObjectiveValue, getGoalTypegetLowerBound, getStartPoint, getUpperBoundgetConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount, optimizepublic SimplexOptimizer(ConvergenceChecker<PointValuePair> checker)
checker - Convergence checker.public SimplexOptimizer(double rel,
double abs)
rel - Relative threshold.abs - Absolute threshold.public PointValuePair optimize(OptimizationData... optData)
The list of parameters is open-ended so that sub-classes can extend it with arguments specific to their concrete implementations.
When the method is called multiple times, instance data is overwritten only when actually present in the list of arguments: when not specified, data set in a previous call is retained (and thus is optional in subsequent calls).
Important note: Subclasses must override
BaseOptimizer.parseOptimizationData(OptimizationData[]) if they need to register
their own options; but then, they must also call
super.parseOptimizationData(optData) within that method.
optimize in class MultivariateOptimizeroptData - Optimization data. In addition to those documented in
MultivariateOptimizer, this method will register the following data:
protected PointValuePair doOptimize()
doOptimize in class BaseOptimizer<PointValuePair>protected void parseOptimizationData(OptimizationData... optData)
parseOptimizationData in class MultivariateOptimizeroptData - Optimization data.
The following data will be looked for:
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