TrustRegionReflectiveOptimizer Class

public sealed class TrustRegionReflectiveOptimizer : LeastSquaresOptimizer

Public NotInheritable Class TrustRegionReflectiveOptimizer
	Inherits LeastSquaresOptimizer

public ref class TrustRegionReflectiveOptimizer sealed : public LeastSquaresOptimizer

[<SealedAttribute>]
type TrustRegionReflectiveOptimizer =  
    class
        inherit LeastSquaresOptimizer
    end

The TrustRegionReflectiveOptimizer type exposes the following members.

	Name	Description
	TrustRegionReflectiveOptimizer	Constructs a new TrustRegionReflectiveOptimizer object.

	Name	Description
	ConvergenceTests	Gets the collection of convergence tests for the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	Dimensions	Gets or sets the number of dimensions of the optimization problem. (Inherited from LeastSquaresOptimizer.)
	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	Obsolete. Gets or sets the current best approximation to the extremum. (Inherited from LeastSquaresOptimizer.)
	GradientTest	Gets the VectorConvergenceTestT that uses the gradient of the objective function. (Inherited from LeastSquaresOptimizer.)
	GradientVector	Gets or sets the current value of the gradient. (Inherited from LeastSquaresOptimizer.)
	HasSharedDegreeOfParallelism	Indicates whether the degree of parallelism is a property that is shared across instances. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	InitialGuess	Gets or sets the initial value for the iteration. (Inherited from LeastSquaresOptimizer.)
	InitialTrustRegionSize	Gets or sets the initial size of the trust region. (Inherited from LeastSquaresOptimizer.)
	IterationsNeeded	Gets the number of iterations needed by the algorithm to reach the desired accuracy. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	JacobianEvaluationsNeeded	Gets or sets the number of times the Jacobian was evaluated. (Inherited from LeastSquaresOptimizer.)
	JacobianFunction	Gets or sets a delegate that computes the Jacobian of the problem. (Inherited from LeastSquaresOptimizer.)
	LowerBounds	Gets or sets the vector of lower bounds for the solution. (Inherited from LeastSquaresOptimizer.)
	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.)
	Minimum	Gets or sets the current best approximation to the minimum. (Inherited from LeastSquaresOptimizer.)
	MinimumValue	Gets or sets the current value of the objective function. (Inherited from LeastSquaresOptimizer.)
	MinIterations	Gets or sets the minimum iterations that have to be performed. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	NumberOfFunctions	Gets or sets the number of functions in the problem. (Inherited from LeastSquaresOptimizer.)
	ObjectiveFunction	Gets or sets a delegate that computes the function values of the problem. (Inherited from LeastSquaresOptimizer.)
	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 VectorConvergenceTestT that uses the approximate solution. (Inherited from LeastSquaresOptimizer.)
	Status	Gets the AlgorithmStatus following an execution of the algorithm. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	ThrowExceptionOnFailure	Gets or sets a value indicating whether to throw an exception when the algorithm fails to converge. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	UpperBounds	Gets or sets the upper bounds for the solution. (Inherited from LeastSquaresOptimizer.)
	ValueAtExtremum	Obsolete. Gets or sets the current value of the objective function. (Inherited from LeastSquaresOptimizer.)
	ValueTest	Gets the SimpleConvergenceTestT that uses the value of the target functions. (Inherited from LeastSquaresOptimizer.)
	VarianceCovarianceMatrix	Gets the variance-covariance matrix of the solution. (Inherited from LeastSquaresOptimizer.)

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	FindMinimum	Runs the Levenberg-Marquardt algorithm and returns the result. (Inherited from LeastSquaresOptimizer.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	SetSymbolicObjectiveFunction(ExpressionFuncVectorDouble, Double)	Sets the objective function as a list of symbolic expressions. (Inherited from LeastSquaresOptimizer.)
	SetSymbolicObjectiveFunction(IListExpressionFuncVectorDouble, Double)	Sets the objective function as a list of symbolic expressions. (Inherited from LeastSquaresOptimizer.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Use the TrustRegionReflectiveOptimizer class to optimize a function that is a sum or weighted sum of squares of functions. This optimizer tends to work somewhat better than the LevenbergMarquardtOptimizer class when the problem is bounded.

The TrustRegionReflectiveOptimizer class inherits from the LeastSquaresOptimizer class.

To verify that the algorithm terminated normally, you can inspect the Status property, which is of type AlgorithmStatus. A value of Converged indicates that the algorithm terminated normally. However, it is still possible that the algorithm didn't converge to the actual best fit. A visual inspection is always recommended.

Reference