MultidimensionalOptimizer Class - Extreme.Mathematics.Optimization - Reference - Documentation - Math, Statistics and Matrix Libraries for .NET in C#, VB and F#

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MultidimensionalOptimizer Class

Extreme Optimization Numerical Libraries for .NET Professional

Represents an algorithm for optimization of a multivariate function.

Inheritance Hierarchy

SystemObject
  Extreme.Mathematics.AlgorithmsManagedIterativeAlgorithmVectorDouble, Double, OptimizationSolutionReport
    Extreme.Mathematics.OptimizationMultidimensionalOptimizer
      Extreme.Mathematics.OptimizationDirectionalOptimizer
      Extreme.Mathematics.OptimizationNelderMeadOptimizer

Namespace: Extreme.Mathematics.Optimization
Assembly: Extreme.Numerics.Net40 (in Extreme.Numerics.Net40.dll) Version: 6.0.16073.0 (6.0.17114.0)

Syntax

C++

Copy

public abstract class MultidimensionalOptimizer : ManagedIterativeAlgorithm<Vector<double>, double, OptimizationSolutionReport>

Public MustInherit Class MultidimensionalOptimizer
	Inherits ManagedIterativeAlgorithm(Of Vector(Of Double), Double, OptimizationSolutionReport)

public ref class MultidimensionalOptimizer abstract : public ManagedIterativeAlgorithm<Vector<double>^, double, OptimizationSolutionReport^>

[<AbstractClassAttribute>]
type MultidimensionalOptimizer =  
    class
        inherit ManagedIterativeAlgorithm<Vector<float>, float, OptimizationSolutionReport>
    end

The MultidimensionalOptimizer type exposes the following members.

Constructors

	Name	Description
	MultidimensionalOptimizer	Constructs a new MultidimensionalOptimizer object.

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Properties

	Name	Description
	ConvergenceTests	Gets the collection of convergence tests for the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ConvergenceTests	Gets the collection of convergence tests for the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	Dimensions	Gets or sets the number of dimensions of the optimization problem.
	EstimatedError	Gets a value indicating the size of the absolute error of the result. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	EstimatedError	Gets a value indicating the size of the absolute error of the result. (Inherited from ManagedIterativeAlgorithmT.)
	EvaluationsNeeded	Gets the number of evaluations needed to execute the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	EvaluationsRemaining	Gets the number of evaluations still available. (Inherited from ManagedIterativeAlgorithmT.)
	Extremum	Gets or sets the current best approximation to the extremum.
	ExtremumType	Gets or sets the type of extremum.
	FastGradientFunction	Gets or sets the function that computes the gradient of the objective funciton.
	GradientEvaluationsNeeded	Gets the number of evaluations of the gradient of the objective function.
	GradientFunction	Gets or sets the function that computes the gradient of the objective funciton.
	GradientTest	Gets the VectorConvergenceTestT that uses the gradient of the objective function.
	GradientVector	Gets or sets the current value of the gradient.
	HasSharedDegreeOfParallelism	Indicates whether the degree of parallelism is a property that is shared across instances. (Inherited from ManagedIterativeAlgorithmT.)
	InitialGuess	Gets or sets the initial value for the iteration.
	IterationsNeeded	Gets the number of iterations needed by the algorithm to reach the desired accuracy. (Inherited from ManagedIterativeAlgorithmT.)
	IterationsRemaining	Gets the number of iterations remaining. (Inherited from ManagedIterativeAlgorithmT.)
	LastCorrection	Gets the last correction to the solution of the system of equations.
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from ManagedIterativeAlgorithmT.)
	MaxEvaluations	Gets or sets the maximum number of evaluations during the calculation. (Inherited from ManagedIterativeAlgorithmT.)
	MaxIterations	Gets or sets the maximum number of iterations to use when approximating the roots of the target function. (Inherited from ManagedIterativeAlgorithmT.)
	MinIterations	Gets or sets the minimum iterations that have to be performed. (Inherited from ManagedIterativeAlgorithmT.)
	ObjectiveFunction	Gets or sets the objective function.
	ParallelOptions	Gets the configuration for the parallel behavior of the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	Result	Gets the result of an algorithm after it has executed. (Inherited from ManagedIterativeAlgorithmT.)
	SolutionReport	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.)
	SolutionTest	Gets the VectorConvergenceTestT that uses the approximate solution.
	Status	Gets the AlgorithmStatus following an execution of the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	SymbolicObjectiveFunction	Gets or sets the objective function.
	ThrowExceptionOnFailure	Gets or sets a value indicating whether to throw an exception when the algorithm fails to converge. (Inherited from ManagedIterativeAlgorithmT.)
	ValueAtExtremum	Gets or sets the current value of the objective function.
	ValueTest	Gets the SimpleConvergenceTestT that uses the value of the target functions.

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Methods

	Name	Description
	CreateSolutionReport	Creates a solution report specific to the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	Equals	Determines whether the specified Object is equal to the current Object. (Inherited from Object.)
	Evaluate(VectorDouble)	Evaluates the objective function.
	Evaluate(Vector)	Evaluates the objective function.
	EvaluateFunctionAndGradient(VectorDouble, VectorDouble)	Evaluates the objective function and its gradient at the same time.
	EvaluateFunctionAndGradient(Vector, Vector)	Evaluates the objective function and its gradient at the same time.
	EvaluateGradient(VectorDouble, VectorDouble)	Evaluates the gradient.
	EvaluateGradient(Vector, Vector)	Evaluates the gradient.
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	FindExtremum	Searches for an extremum.
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	IncrementEvaluations	Increments the number of evaluations by one. (Inherited from ManagedIterativeAlgorithmT.)
	IncrementEvaluations(Int32)	Increments the number of evaluations by the specified amount. (Inherited from ManagedIterativeAlgorithmT.)
	Iterate	Performs one iteration of the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	Iterated	Performs tasks after the iteration is completed, but before the status of the algorithm is finalized. (Inherited from ManagedIterativeAlgorithmT.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	OnConvergence	Performs any tasks after the main algorithm has converged. (Overrides ManagedIterativeAlgorithmT, TError, TReportOnConvergence.)
	OnFailure	Performs any tasks after the main algorithm has failed to converge. (Inherited from ManagedIterativeAlgorithmT.)
	OnInit	Performs initialization before the first iteration. (Overrides ManagedIterativeAlgorithmT, TError, TReportOnInit.)
	ReportFailure(T, TError, AlgorithmStatus)	Records the results of an algorithm in case it fails. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ReportFailure(T, Double, AlgorithmStatus)	Records the results of an algorithm in case it fails. (Inherited from ManagedIterativeAlgorithmT.)
	ReportResult(T, TError)	Records the results of an algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ReportResult(T, Double)	Records the results of an algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	ReportSuccess(T, TError)	Records the results of a algorithm that converged successfully. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ReportSuccess(T, Double)	Records the results of a algorithm that converged successfully. (Inherited from ManagedIterativeAlgorithmT.)
	ResetEvaluations	Resets the number of evaluations to zero. (Inherited from ManagedIterativeAlgorithmT.)
	Restart	Prepares the algorithm to be run again with possibly different inputs. (Inherited from ManagedIterativeAlgorithmT.)
	Run	Runs the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Run	Runs the algorithm. (Inherited from ManagedIterativeAlgorithmT.)
	Run(ParallelOptions)	Runs the algorithm using the specified parallelization options. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Run(ParallelOptions)	Runs the algorithm using the specified parallelization options. (Inherited from ManagedIterativeAlgorithmT.)
	SetResult(T, TError, AlgorithmStatus, Int32)	Sets the results of an algorithm's execution. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	SetResult(T, Double, AlgorithmStatus, Int32)	Sets the results of an algorithm's execution. (Inherited from ManagedIterativeAlgorithmT.)
	SetValueAtExtremum	Sets the value of the objective function at the extremum to the supplied value.
	TestConvergence	Checks whether the algorithm has converged. (Inherited from ManagedIterativeAlgorithmT.)
	ThreadSafeIncrementEvaluations	Increments the number of evaluations by one. (Inherited from ManagedIterativeAlgorithmT.)
	ThreadSafeIncrementEvaluations(Int32)	Increments the number of evaluations by the specified amount. (Inherited from ManagedIterativeAlgorithmT.)
	ThrowConvergenceException	Interprets the AlgorithmStatus and throws the appropriate exception. (Inherited from ManagedIterativeAlgorithmT.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Remarks

MultidimensionalOptimizer is the abstract base class for all algorithms that implement optimization algorithms in multiple dimensions. It inherits from ManagedIterativeAlgorithm. All methods and properties of this class are available to MultidimensionalOptimizer and its derived classes.

MultidimensionalOptimizer is an abstract class and cannot be instantiated directly. Instead, use one of the following derived classes:

Class	Description
ConjugateGradientOptimizer	Represents an optimizer that uses a conjugate gradient algorithm. This class supports the Fletcher-Reeves, Polak-Ribière and positive Polak-Ribière variants.
NelderMeadOptimizer	Represents an optimizer that uses Nelder and Mead's downhill simplex algorithm.
PowellOptimizer	Represents an optimizer that uses Powell's derivative-free conjugate gradient algorithm.
QuasiNewtonOptimizer	Represents an optimizer that uses a quasi-Newton algorithm. Both the DFP (Davison-Fletcher-Powell) and BFGS (Broyden-Fletcher-Goldfard-Shanno) methods are supported.

Version Information

Numerical Libraries

Supported in: 6.0, 5.x, 4.x

Reference

Extreme.Mathematics.Optimization Namespace

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