Implements Powell's modified multidimensional optimization method.
SystemObject Extreme.Mathematics.AlgorithmsManagedIterativeAlgorithmVectorDouble,
Double,
OptimizationSolutionReport Extreme.Mathematics.OptimizationMultidimensionalOptimizer Extreme.Mathematics.OptimizationDirectionalOptimizer Extreme.Mathematics.OptimizationPowellOptimizer
Namespace:
Extreme.Mathematics.Optimization
Assembly:
Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
public sealed class PowellOptimizer : DirectionalOptimizer
Public NotInheritable Class PowellOptimizer
Inherits DirectionalOptimizer
public ref class PowellOptimizer sealed : public DirectionalOptimizer
[<SealedAttribute>]
type PowellOptimizer =
class
inherit DirectionalOptimizer
end
The PowellOptimizer type exposes the following members.
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| Name | Description |
---|
| ConvergenceTests |
Gets the collection of convergence tests for the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| CurrentDirection |
Gets or sets the current search direction.
(Inherited from DirectionalOptimizer.) |
| Dimensions |
Gets or sets the number of dimensions of the optimization problem.
(Inherited from MultidimensionalOptimizer.) |
| EstimatedError |
Gets a value indicating the size of the absolute
error of the result.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| EvaluationsNeeded |
Gets the number of evaluations needed to execute the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| Extremum |
Gets or sets the current best approximation to the extremum.
(Inherited from MultidimensionalOptimizer.) |
| ExtremumType |
Gets or sets the type of extremum.
(Inherited from MultidimensionalOptimizer.) |
| FastGradientFunction |
Gets or sets the function that computes the gradient of the objective funciton.
(Inherited from MultidimensionalOptimizer.) |
| GradientEvaluationsNeeded |
Gets the number of evaluations of the gradient of the objective function.
(Inherited from MultidimensionalOptimizer.) |
| GradientFunction |
Gets or sets the function that computes the gradient of the objective funciton.
(Inherited from MultidimensionalOptimizer.) |
| GradientTest | (Inherited from MultidimensionalOptimizer.) |
| GradientVector |
Gets or sets the current value of the gradient.
(Inherited from MultidimensionalOptimizer.) |
| HasSharedDegreeOfParallelism |
Indicates whether the degree of parallelism is a property that is shared
across instances.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| InitialGuess |
Gets or sets the initial value for the iteration.
(Inherited from MultidimensionalOptimizer.) |
| IterationsNeeded |
Gets the number of iterations needed by the
algorithm to reach the desired accuracy.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| LineSearch |
Gets or sets the algorithm used to perform a line search.
(Inherited from DirectionalOptimizer.) |
| MaxDegreeOfParallelism |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| MaxEvaluations |
Gets or sets the maximum number of evaluations during the calculation.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| MaxIterations | Gets or sets the maximum number of iterations
to use when approximating the roots of the target
function.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| MinIterations |
Gets or sets the minimum iterations that have to be performed.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| ObjectiveFunction |
Gets or sets the objective function.
(Inherited from MultidimensionalOptimizer.) |
| ObjectiveFunctionWithGradient |
Gets or sets a function that evaluates the value and gradient
of the objective function.
(Inherited from MultidimensionalOptimizer.) |
| ParallelOptions |
Gets or sets the configuration for the parallel behavior of the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| Result |
Gets the result of an algorithm after it has executed.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| SolutionReport |
Gets the result of an algorithm after it has executed.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| SolutionTest | (Inherited from MultidimensionalOptimizer.) |
| Status | (Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| SymbolicObjectiveFunction |
Gets or sets the objective function.
(Inherited from MultidimensionalOptimizer.) |
| ThrowExceptionOnFailure |
Gets or sets a value indicating whether to throw an
exception when the algorithm fails to converge.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.) |
| ValueAtExtremum |
Gets or sets the current value of the objective function.
(Inherited from MultidimensionalOptimizer.) |
| ValueTest | (Inherited from MultidimensionalOptimizer.) |
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Use the PowellOptimizer class to find an extremum of a multivariate function
using Powell's direction set method. The method is useful when the gradient of the objective function
is not available. Still, this method is usually less efficient than either
a quasi-Newton or
a full conjugate gradient method
with a numerical approximation of the gradient.
It is here mostly for its historical importance.
The objective function must be supplied as a multivariate function
delegate to the ObjectiveFunction property. The gradient of the objective function
can be supplied either as a multivariate function returning a vector delegate (by setting the
GradientFunction property), or a multivariate function returning a vector in its second argument
delegate (by setting the FastGradientFunction property). The latter has the advantage
that the same Vector instance is reused to hold the result.
Before the algorithm is run, you must set the InitialGuess property to
a vector that contains an initial estimate for the extremum. The ExtremumType
property specifies whether a minimum or a maximum of the objective function is desired.
The FindExtremum method performs the actual
search for an extremum, and returns a Vector containing the best approximation.
The Extremum property also returns the best
approximation to the extremum. The ValueAtExtremum property
returns the value of the objective function at the extremum.
The Status
property is a AlgorithmStatus value that indicates the outcome of the algorithm.
A value of Normal shows normal termination.
A value of Divergent usually indicates that the objective
function is not bounded.
The algorithm has two convergence tests. By default, the algorithm terminates
when either of these is satisfied. You can deactivate either test by setting its Enabled
property to . If both tests are deactivated, then the algorithm always terminates when
the maximum number of iterations or function evaluations is reached.
The first test is based on the uncertainty in the location
of the approximate extremum. The SolutionTest property returns a
VectorConvergenceTestT object that allows you to specify the desired
Tolerance and
specific ConvergenceCriterion.
See the VectorConvergenceTestT class for details on how to further customize
this test.
The second test is based on the change in value of the objective function at the approximate extremum.
The test is successful when the change of the value of the objective function is within the tolerance.
Care should be taken with this test. When the tolerance is too large, the algorithm will terminate prematurely.
The ValueTest property returns a SimpleConvergenceTestT object
that can be used to customize the test.
A third test, based on the value of the gradient at the approximate extremum,
is exposed through the GradientTest property.
However, this test does not apply to Powell's method, and it's
Enabled property is set to
before the algorithm is executed.
Reference