public abstract class LeastSquaresOptimizer : ManagedIterativeAlgorithm<Vector<double>>

Public MustInherit Class LeastSquaresOptimizer
	Inherits ManagedIterativeAlgorithm(Of Vector(Of Double))

public ref class LeastSquaresOptimizer abstract : public ManagedIterativeAlgorithm<Vector<double>^>

[<AbstractClassAttribute>]
type LeastSquaresOptimizer =  
    class
        inherit ManagedIterativeAlgorithm<Vector<float>>
    end

The LeastSquaresOptimizer type exposes the following members.

	Name	Description
	LeastSquaresOptimizer	Constructs a new LeastSquaresOptimizer 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.
	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	Obsolete. Gets or sets the current best approximation to the extremum.
	GradientTest	Gets the VectorConvergenceTestT that uses the gradient of the objective function.
	GradientVector	Gets or sets the current value of the gradient.
	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.
	InitialTrustRegionSize	Gets or sets the initial size of the trust region.
	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.)
	JacobianEvaluationsNeeded	Gets or sets the number of times the Jacobian was evaluated.
	JacobianFunction	Gets or sets a delegate that computes the Jacobian of the problem.
	LastCorrection	Gets the last correction to the solution of the system of equations.
	LowerBounds	Gets or sets the vector of lower bounds for the solution.
	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.
	MinimumValue	Gets or sets the current value of the objective function.
	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.
	ObjectiveFunction	Gets or sets a delegate that computes the function values of the problem.
	ParallelOptions	Gets 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.
	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.
	ValueAtExtremum	Obsolete. Gets or sets the current value of the objective function.
	ValueTest	Gets the SimpleConvergenceTestT that uses the value of the target functions.
	VarianceCovarianceMatrix	Gets the variance-covariance matrix of the solution.

	Name	Description
	Equals	Determines whether the specified Object is equal to the current Object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	FindMinimum	Runs the Levenberg-Marquardt algorithm and returns the result.
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetVarianceCovarianceMatrix	Computes the variance-covariance matrix of the solution.
	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. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	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. (Overrides ManagedIterativeAlgorithmT, TError, TReportOnConvergence.)
	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 ManagedIterativeAlgorithmT, TError, TReportOnInit.)
	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.)
	SetMinimumValue	Sets the value of the objective function at the minimum to the supplied value.
	SetResult	Sets the results of an algorithm's execution. (Inherited from ManagedIterativeAlgorithmT, TError, TReport.)
	SetSymbolicObjectiveFunction(IListExpressionFuncVectorDouble, Double)	Sets the objective function as a list of symbolic expressions.
	SetSymbolicObjectiveFunction(ExpressionFuncVectorDouble, Double)	Sets the objective function as a list of symbolic expressions.
	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.)

The LeastSquaresOptimizer class serves as the abstract base class for implementations of nonlinear least squares optimizers. This class cannot be instantiated. Instead, use one of its inherited classes:

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.

Numerical Libraries

Reference