More Thuente Line Search Class
Definition
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.0.4
public sealed class MoreThuenteLineSearch : OneDimensionalOptimizer
- Inheritance
- Object → ManagedIterativeAlgorithm<Double, Double, SolutionReport<Double, Double>> → ManagedIterativeAlgorithm<Double> → OneDimensionalOptimizer → MoreThuenteLineSearch
Remarks
Use the MoreThuenteLineSearch class as a line search method in a multidimensional optimization algorithm. This class is not suitable for general optimization in one dimension.
The Moré-Thuente algorithm is the line search method of choice for most applications, particularly when the gradient of the objective function is relatively cheap. 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 Wolfe conditions as termination criteria. The parameters of the Wolfe conditions can be set or retrieved through the DescentFactor and CurvatureFactor, respectively.
Conjugate gradient methods, including Powell's method, require fairly precise line searches. This means many function evaluations may be needed, which can get expensive when evaluating the gradient is expensive.
Constructors
More | Constructs a new MoreThuenteLineSearch object. |
More | Constructs a new MoreThuenteLineSearch object. |
Properties
Convergence |
Gets the collection of convergence tests for the algorithm.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Curvature | Gets or sets the factor used in the curvature condition of the line search termination criteria. |
Derivative |
Gets or sets the derivative of the objective function.
(Inherited from OneDimensionalOptimizer) |
Descent | Gets or sets the factor in the sufficient descent condition. |
Estimated |
Gets a value indicating the size of the absolute
error of the result.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Evaluations |
Gets the number of evaluations needed to execute the algorithm.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Extremum |
Gets the approximation to the extremum after the algorithm has run.
(Inherited from OneDimensionalOptimizer) |
Extremum |
Gets or sets the type of extremum.
(Inherited from OneDimensionalOptimizer) |
Has |
Indicates whether the degree of parallelism is a property that is shared
across instances.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
IsBracket |
Gets whether the algorithm's current bracket is valid.
(Inherited from OneDimensionalOptimizer) |
Iterations |
Gets the number of iterations needed by the
algorithm to reach the desired accuracy.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Max |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Max |
Gets or sets the maximum number of evaluations during the calculation.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Max | Gets or sets the maximum number of iterations
to use when approximating the roots of the target
function.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Max | Gets or sets the largest allowed step length. |
Min |
Gets or sets the minimum iterations that have to be performed.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Objective |
Gets or sets the objective function.
(Inherited from OneDimensionalOptimizer) |
Objective |
Gets or sets a function that computes the value of the objective function
and its derivative.
(Inherited from OneDimensionalOptimizer) |
Parallel |
Gets or sets the configuration for the parallel behavior of the algorithm.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Result |
Gets the result of an algorithm after it has executed.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Solution |
Gets the result of an algorithm after it has executed.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Solution |
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 ManagedIterativeAlgorithm<T, TError, TReport>) |
Symbolic |
Gets or sets the objective function.
(Inherited from OneDimensionalOptimizer) |
Throw |
Gets or sets whether to throw an
exception when the algorithm fails to converge.
(Inherited from ManagedIterativeAlgorithm<T, TError, TReport>) |
Value |
Gets the value of the objective function at the approximation to the extremum after the algorithm has run.
(Inherited from OneDimensionalOptimizer) |
Methods
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
Find |
Finds an interval that brackets the extremum, starting from the interval [0,1].
(Inherited from OneDimensionalOptimizer) |
Find |
Finds an interval that brackets the extremum, starting from an interval of unit width centered around the specified point.
(Inherited from OneDimensionalOptimizer) |
Find |
Finds an interval that brackets the extremum, starting from an interval with the specified bounds.
(Inherited from OneDimensionalOptimizer) |
Find |
Finds an interval that brackets the extremum, starting from an interval with the specified bounds and
interior point.
(Inherited from OneDimensionalOptimizer) |
Find |
Searches for an extremum.
(Inherited from OneDimensionalOptimizer) |
Find |
Computes a maximum of the specified function.
(Inherited from OneDimensionalOptimizer) |
Find |
Computes a maximum of the specified function.
(Inherited from OneDimensionalOptimizer) |
Find |
Computes a minimum of the specified function.
(Inherited from OneDimensionalOptimizer) |
Find |
Computes a minimum of the specified function.
(Inherited from OneDimensionalOptimizer) |
Get | Serves as the default hash function. (Inherited from Object) |
Get | Gets the Type of the current instance. (Inherited from Object) |
ToString | Returns a string that represents the current object. (Inherited from Object) |