Package org.hipparchus.optim.nonlinear.vector.leastsquares

package org.hipparchus.optim.nonlinear.vector.leastsquares

This package provides algorithms that minimize the residuals between observations and model values. The least-squares optimizers minimize the distance (called cost or χ²) between model and observations.
Algorithms in this category need access to a problem (represented by a LeastSquaresProblem). Such a model predicts a set of values which the algorithm tries to match with a set of given set of observed values.
The problem can be created progressively using a builder or it can be created at once using a factory.

Class	Description
AbstractEvaluation	An implementation of LeastSquaresProblem.Evaluation that is designed for extension.
EvaluationRmsChecker	Check if an optimization has converged based on the change in computed RMS.
GaussNewtonOptimizer	Gauss-Newton least-squares solver.
LeastSquaresAdapter	An adapter that delegates to another implementation of LeastSquaresProblem.
LeastSquaresBuilder	A mutable builder for LeastSquaresProblems.
LeastSquaresFactory	A Factory for creating LeastSquaresProblems.
LeastSquaresOptimizer	An algorithm that can be applied to a non-linear least squares problem.
LeastSquaresOptimizer.Optimum	The optimum found by the optimizer.
LeastSquaresProblem	The data necessary to define a non-linear least squares problem.
LeastSquaresProblem.Evaluation	An evaluation of a LeastSquaresProblem at a particular point.
LevenbergMarquardtOptimizer	This class solves a least-squares problem using the Levenberg-Marquardt algorithm.
MultivariateJacobianFunction	A interface for functions that compute a vector of values and can compute their derivatives (Jacobian).
ParameterValidator	Interface for validating a set of model parameters.
SequentialGaussNewtonOptimizer	Sequential Gauss-Newton least-squares solver.
ValueAndJacobianFunction	A interface for functions that compute a vector of values and can compute their derivatives (Jacobian).