Uses of Interface
org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem

Packages that use LeastSquaresProblem

Package	Description
org.hipparchus.optim.nonlinear.vector.leastsquares	This package provides algorithms that minimize the residuals between observations and model values.

Uses of LeastSquaresProblem in org.hipparchus.optim.nonlinear.vector.leastsquares

Classes in org.hipparchus.optim.nonlinear.vector.leastsquares that implement LeastSquaresProblem

Modifier and Type	Class	Description
class	LeastSquaresAdapter	An adapter that delegates to another implementation of LeastSquaresProblem.

Methods in org.hipparchus.optim.nonlinear.vector.leastsquares that return LeastSquaresProblem

Modifier and Type	Method	Description
LeastSquaresProblem	LeastSquaresBuilder.build()	Construct a LeastSquaresProblem from the data in this builder.
static LeastSquaresProblem	LeastSquaresFactory.countEvaluations(LeastSquaresProblem problem, Incrementor counter)	Count the evaluations of a particular problem.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations, boolean lazyEvaluation, ParameterValidator paramValidator)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.create(MultivariateJacobianFunction model, RealVector observed, RealVector start, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations)	Create a LeastSquaresProblem from the given elements.
static LeastSquaresProblem	LeastSquaresFactory.weightDiagonal(LeastSquaresProblem problem, RealVector weights)	Apply a diagonal weight matrix to the LeastSquaresProblem.
static LeastSquaresProblem	LeastSquaresFactory.weightMatrix(LeastSquaresProblem problem, RealMatrix weights)	Apply a dense weight matrix to the LeastSquaresProblem.

Methods in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type LeastSquaresProblem

Modifier and Type	Method	Description
static LeastSquaresProblem	LeastSquaresFactory.countEvaluations(LeastSquaresProblem problem, Incrementor counter)	Count the evaluations of a particular problem.
LeastSquaresOptimizer.Optimum	GaussNewtonOptimizer.optimize(LeastSquaresProblem lsp)	Solve the non-linear least squares problem.
LeastSquaresOptimizer.Optimum	LeastSquaresOptimizer.optimize(LeastSquaresProblem leastSquaresProblem)	Solve the non-linear least squares problem.
LeastSquaresOptimizer.Optimum	LevenbergMarquardtOptimizer.optimize(LeastSquaresProblem problem)	Solve the non-linear least squares problem.
LeastSquaresOptimizer.Optimum	SequentialGaussNewtonOptimizer.optimize(LeastSquaresProblem lsp)	Solve the non-linear least squares problem.
static LeastSquaresProblem	LeastSquaresFactory.weightDiagonal(LeastSquaresProblem problem, RealVector weights)	Apply a diagonal weight matrix to the LeastSquaresProblem.
static LeastSquaresProblem	LeastSquaresFactory.weightMatrix(LeastSquaresProblem problem, RealMatrix weights)	Apply a dense weight matrix to the LeastSquaresProblem.

Constructors in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type LeastSquaresProblem

Modifier	Constructor	Description
	LeastSquaresAdapter(LeastSquaresProblem problem)	Delegate the LeastSquaresProblem interface to the given implementation.