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  • Extreme.Mathematics.Optimization
    • BoundedQuasiNewtonOptimizer Class
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    • OneDimensionalOptimizer Class
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  • BrentOptimizer Class
    • BrentOptimizer Constructors
    • BrentOptimizer Properties
    • BrentOptimizer Methods

BrentOptimizer Class

Extreme Optimization Numerical Libraries for .NET Professional
Represents a one-dimensional optimizer based on Brent's algorithm.
Inheritance Hierarchy

SystemObject
  Extreme.Mathematics.AlgorithmsManagedIterativeAlgorithmDouble, Double, SolutionReportDouble, Double
    Extreme.Mathematics.AlgorithmsManagedIterativeAlgorithmDouble
      Extreme.Mathematics.OptimizationOneDimensionalOptimizer
        Extreme.Mathematics.OptimizationBrentOptimizer

Namespace:  Extreme.Mathematics.Optimization
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
Syntax

C#
VB
C++
F#
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public sealed class BrentOptimizer : OneDimensionalOptimizer
Public NotInheritable Class BrentOptimizer
	Inherits OneDimensionalOptimizer
public ref class BrentOptimizer sealed : public OneDimensionalOptimizer
[<SealedAttribute>]
type BrentOptimizer =  
    class
        inherit OneDimensionalOptimizer
    end

The BrentOptimizer type exposes the following members.

Constructors

  NameDescription
Public methodBrentOptimizer
Constructs a new BrentOptimizer object.
Public methodBrentOptimizer(ExtremumType)
Constructs a new BrentOptimizer object for the specified type of extremum.
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Properties

  NameDescription
Public propertyConvergenceTests
Gets the collection of convergence tests for the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyDerivativeOfObjectiveFunction
Gets or sets the derivative of the objective function.
(Inherited from OneDimensionalOptimizer.)
Public propertyEstimatedError
Gets a value indicating the size of the absolute error of the result.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyEvaluationsNeeded
Gets the number of evaluations needed to execute the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyExtremum
Gets the approximation to the extremum after the algorithm has run.
(Inherited from OneDimensionalOptimizer.)
Public propertyExtremumType
Gets or sets the type of extremum.
(Inherited from OneDimensionalOptimizer.)
Public propertyHasSharedDegreeOfParallelism
Indicates whether the degree of parallelism is a property that is shared across instances.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyIsBracketValid
Gets whether the algorithm's current bracket is valid.
(Inherited from OneDimensionalOptimizer.)
Public propertyIterationsNeeded
Gets the number of iterations needed by the algorithm to reach the desired accuracy.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyMaxDegreeOfParallelism
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyMaxEvaluations
Gets or sets the maximum number of evaluations during the calculation.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyMaxIterations
Gets or sets the maximum number of iterations to use when approximating the roots of the target function.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyMinIterations
Gets or sets the minimum iterations that have to be performed.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyObjectiveFunction
Gets or sets the objective function.
(Inherited from OneDimensionalOptimizer.)
Public propertyObjectiveFunctionWithDerivative
Gets or sets a function that computes the value of the objective function and its derivative.
(Inherited from OneDimensionalOptimizer.)
Public propertyParallelOptions
Gets or sets the configuration for the parallel behavior of the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyResult
Gets the result of an algorithm after it has executed.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertySolutionReport
Gets the result of an algorithm after it has executed.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertySolutionTest
Gets the convergence test that uses the solution of the optimization.
(Inherited from OneDimensionalOptimizer.)
Public propertyStatus
Gets the AlgorithmStatus following an execution of the algorithm.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertySymbolicObjectiveFunction
Gets or sets the objective function.
(Inherited from OneDimensionalOptimizer.)
Public propertyThrowExceptionOnFailure
Gets or sets a value indicating whether to throw an exception when the algorithm fails to converge.
(Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
Public propertyValueAtExtremum
Gets the value of the objective function at the approximation to the extremum after the algorithm has run.
(Inherited from OneDimensionalOptimizer.)
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Methods

  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Public methodFindBracket
Finds an interval that brackets the extremum, starting from the interval [0,1].
(Inherited from OneDimensionalOptimizer.)
Public methodFindBracket(Double)
Finds an interval that brackets the extremum, starting from an interval of unit width centered around the specified point.
(Inherited from OneDimensionalOptimizer.)
Public methodFindBracket(Double, Double)
Finds an interval that brackets the extremum, starting from an interval with the specified bounds.
(Inherited from OneDimensionalOptimizer.)
Public methodFindBracket(Double, Double, Double)
Finds an interval that brackets the extremum, starting from an interval with the specified bounds and interior point.
(Inherited from OneDimensionalOptimizer.)
Public methodFindExtremum
Searches for an extremum.
(Inherited from OneDimensionalOptimizer.)
Public methodFindMaximum(FuncDouble, Double, Double)
Computes a maximum of the specified function.
(Inherited from OneDimensionalOptimizer.)
Public methodFindMaximum(FuncDouble, Double, Double, Double)
Computes a maximum of the specified function.
(Inherited from OneDimensionalOptimizer.)
Public methodFindMinimum(FuncDouble, Double, Double)
Computes a minimum of the specified function.
(Inherited from OneDimensionalOptimizer.)
Public methodFindMinimum(FuncDouble, Double, Double, Double)
Computes a minimum of the specified function.
(Inherited from OneDimensionalOptimizer.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Remarks

Use the BrentOptimizer class to find a minimum or maximum of a function when the derivative of the objective function is not available or very expensive to calculate.

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) or FindMinimum(FuncDouble, 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.

The algorithm uses Brent's original algorithm. In each iteration of the location phase, either a Golden Section step is taken, or a new approximation is calculated using quadratic or cubic interpolation. This method is the most robust method available for optimization in one dimension.

See Also

Reference

Extreme.Mathematics.Optimization Namespace
Extreme.Mathematics.OptimizationGoldenSectionOptimizer
Extreme.Mathematics.OptimizationBrentDerivativeOptimizer

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