GoldenSectionOptimizer Class

public class GoldenSectionOptimizer : OneDimensionalOptimizer

Public Class GoldenSectionOptimizer
	Inherits OneDimensionalOptimizer

public ref class GoldenSectionOptimizer : public OneDimensionalOptimizer

type GoldenSectionOptimizer =  
    class
        inherit OneDimensionalOptimizer
    end

The GoldenSectionOptimizer type exposes the following members.

	Name	Description
	GoldenSectionOptimizer	Constructs a new GoldenSectionOptimizer object.
	GoldenSectionOptimizer(ExtremumType)	Constructs a new GoldenSectionOptimizer object.

	Name	Description
	ConvergenceTests	Gets the collection of convergence tests for the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	DerivativeOfObjectiveFunction	Gets or sets the derivative of the objective function. (Inherited from OneDimensionalOptimizer.)
	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.)
	EvaluationsRemaining	Gets the number of evaluations still available. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Extremum	Gets the approximation to the extremum after the algorithm has run. (Inherited from OneDimensionalOptimizer.)
	ExtremumType	Gets or sets the type of extremum. (Inherited from OneDimensionalOptimizer.)
	HasSharedDegreeOfParallelism	Indicates whether the degree of parallelism is a property that is shared across instances. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	IsBracketValid	Gets whether the algorithm's current bracket is valid. (Inherited from OneDimensionalOptimizer.)
	IterationsNeeded	Gets the number of iterations needed by the algorithm to reach the desired accuracy. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	IterationsRemaining	Gets the number of iterations remaining. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	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 OneDimensionalOptimizer.)
	ObjectiveFunctionWithDerivative	Gets or sets a function that computes the value of the objective function and its derivative. (Inherited from OneDimensionalOptimizer.)
	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	Gets the convergence test that uses the solution of the optimization. (Inherited from OneDimensionalOptimizer.)
	Status	Gets the AlgorithmStatus following an execution of the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	SymbolicObjectiveFunction	Gets or sets the objective function. (Inherited from OneDimensionalOptimizer.)
	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 the value of the objective function at the approximation to the extremum after the algorithm has run. (Inherited from OneDimensionalOptimizer.)

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Evaluate	Evaluates the objective function. (Inherited from OneDimensionalOptimizer.)
	EvaluateDerivative	Evaluates the derivative of the objective function. (Inherited from OneDimensionalOptimizer.)
	EvaluateWithDerivative	Evaluates the objective function and its derivative. (Inherited from OneDimensionalOptimizer.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	FindBracket	Finds an interval that brackets the extremum, starting from the interval [0,1]. (Inherited from OneDimensionalOptimizer.)
	FindBracket(Double)	Finds an interval that brackets the extremum, starting from an interval of unit width centered around the specified point. (Inherited from OneDimensionalOptimizer.)
	FindBracket(Double, Double)	Finds an interval that brackets the extremum, starting from an interval with the specified bounds. (Inherited from OneDimensionalOptimizer.)
	FindBracket(Double, Double, Double)	Finds an interval that brackets the extremum, starting from an interval with the specified bounds and interior point. (Inherited from OneDimensionalOptimizer.)
	FindExtremum	Searches for an extremum. (Inherited from OneDimensionalOptimizer.)
	FindMaximum(FuncDouble, Double, Double)	Computes a maximum of the specified function. (Inherited from OneDimensionalOptimizer.)
	FindMaximum(FuncDouble, Double, Double, Double)	Computes a maximum of the specified function. (Inherited from OneDimensionalOptimizer.)
	FindMinimum(FuncDouble, Double, Double)	Computes a minimum of the specified function. (Inherited from OneDimensionalOptimizer.)
	FindMinimum(FuncDouble, Double, Double, Double)	Computes a minimum of the specified function. (Inherited from OneDimensionalOptimizer.)
	GetHashCode	Serves as the default hash function. (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, TError, TReport.)
	IncrementEvaluations(Int32)	Increments the number of evaluations by the specified amount. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Iterate	Performs one iteration of the algorithm. (Overrides ManagedIterativeAlgorithmT, TError, TReportIterate.)
	Iterated	Performs tasks after the iteration is completed, but before the status of the algorithm is finalized. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	OnConvergence	Performs any tasks after the main algorithm has converged. (Inherited from OneDimensionalOptimizer.)
	OnFailure	Performs any tasks after the main algorithm has failed to converge. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	OnInit	Performs initialization before the first iteration. (Overrides OneDimensionalOptimizerOnInit.)
	ReportFailure	Records the results of an algorithm in case it fails. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ReportResult	Records the results of an algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ReportSuccess	Records the results of a algorithm that converged successfully. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ResetEvaluations	Resets the number of evaluations to zero. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Restart	Prepares the algorithm to be run again with possibly different inputs. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Run	Runs the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Run(ParallelOptions)	Runs the algorithm using the specified parallelization options. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	SetResult	Sets the results of an algorithm's execution. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	TestConvergence	Checks whether the algorithm has converged. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ThreadSafeIncrementEvaluations	Increments the number of evaluations by one. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ThreadSafeIncrementEvaluations(Int32)	Increments the number of evaluations by the specified amount. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ThrowConvergenceException	Interprets the AlgorithmStatus and throws the appropriate exception. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Use the GoldenSectionOptimizer to find the minimum or a maximum of a function.

The ObjectiveFunction property must be set to a function of one variable that evaluates the objective function. The ExtremumType property specifies whether a maximum or a minimum of the objective function is requested.

The algorithm itself runs in two phases. In the bracketing phase, a search is made for an interval that is known to contain an extremum. This step is performed automatically when the algorithm is run. You can run it manually by calling one of the FindBracket(Double) methods. You can check the validity of a bracketing interval by inspecting the IsBracketValid property.

Once a bracketing interval has been found, the location phase begins. The exact location of the extremum is found by successively narrowing the bracketing interval. This phase always converges for continuous functions. The FindExtremum method performs the location phase, and returns the best approximation to the extremum. Alternatively, one of the FindMaximum(FuncDouble, Double, Double, Double) or FindMinimum(FuncDouble, Double, Double, Double) methods can be used. This has the advantage that the objective function as well as an initial guess can be supplied with the method call.

The Extremum property returns the best approximation to the extremum. The EstimatedError property returns the uncertainty of 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 a bracketing interval could not be found.

Convergence is tested using a simple convergence test based on the uncertainty in the location of the approximate extremum. The SolutionTest property returns a SimpleConvergenceTestT object that allows you to specify the desired Tolerance and specific ConvergenceCriterion.

Reference