BacktrackingLineSearch Class

public sealed class BacktrackingLineSearch : OneDimensionalOptimizer

Public NotInheritable Class BacktrackingLineSearch
	Inherits OneDimensionalOptimizer

public ref class BacktrackingLineSearch sealed : public OneDimensionalOptimizer

[<SealedAttribute>]
type BacktrackingLineSearch =  
    class
        inherit OneDimensionalOptimizer
    end

The BacktrackingLineSearch type exposes the following members.

	Name	Description
	BacktrackingLineSearch(MultidimensionalOptimizer)	Constructs a new BacktrackingLineSearch object.
	BacktrackingLineSearch(MultidimensionalOptimizer, Double, Double)	Constructs a new BacktrackingLineSearch object.
	BacktrackingLineSearch(MultidimensionalOptimizer, Double, Double, Double)	Constructs a new BacktrackingLineSearch 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.)
	DescentFactor	Gets or sets the factor in the sufficient descent condition.
	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 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.)
	MaxContractionFactor	Gets or sets the largest allowed contraction factor between steps in the backtracking procedure.
	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.)
	MaxStepLength	Gets or sets the largest allowed step length.
	MinContractionFactor	Gets or sets the smallest allowed contraction factor between steps in the backtracking procedure.
	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.)
	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.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Use the BacktrackingLineSearch class as a line search method in a multidimensional optimization algorithm. This class is not suitable for general optimization in one dimension.

The backtracking algorithm is the line search method of choice for most applications, particularly when the gradient of the objective function is relatively expensive. The algorithm uses a custom termination criterion based on the Wolfe conditions. These conditions guarantee convergence of quasi-Newton algorithms in most situations.

The algorithm uses the Armijo conditions as termination criteria. The parameter of the Armijo conditions can be set or retrieved through the DescentFactor property, while the rate of backtracking can be controlled through the MinContractionFactor and MaxContractionFactor properties.

Reference