Uses of Interface
org.hipparchus.linear.MatrixDecomposer

Packages that use MatrixDecomposer

Package	Description
org.hipparchus.analysis.differentiation	This package holds the main interfaces and basic building block classes dealing with differentiation.
org.hipparchus.filtering.kalman	Kalman filter.
org.hipparchus.filtering.kalman.extended	Kalman filter implementation for non-linear processes.
org.hipparchus.filtering.kalman.linear	Kalman filter implementation for linear processes.
org.hipparchus.filtering.kalman.unscented	Unscented Kalman filter implementation.
org.hipparchus.linear	Linear algebra support.
org.hipparchus.optim.nonlinear.vector.leastsquares	This package provides algorithms that minimize the residuals between observations and model values.

Uses of MatrixDecomposer in org.hipparchus.analysis.differentiation

Methods in org.hipparchus.analysis.differentiation with parameters of type MatrixDecomposer

Modifier and Type	Method	Description
TaylorMap	TaylorMap.invert(MatrixDecomposer decomposer)	Invert the instance.

Uses of MatrixDecomposer in org.hipparchus.filtering.kalman

Constructors in org.hipparchus.filtering.kalman with parameters of type MatrixDecomposer

Constructor	Description
AbstractKalmanFilter(MatrixDecomposer decomposer, ProcessEstimate initialState)	Simple constructor.
KalmanSmoother(MatrixDecomposer decomposer)	Simple constructor.

Uses of MatrixDecomposer in org.hipparchus.filtering.kalman.extended

Constructors in org.hipparchus.filtering.kalman.extended with parameters of type MatrixDecomposer

Constructor	Description
ExtendedKalmanFilter(MatrixDecomposer decomposer, NonLinearProcess<T> process, ProcessEstimate initialState)	Simple constructor.

Uses of MatrixDecomposer in org.hipparchus.filtering.kalman.linear

Constructors in org.hipparchus.filtering.kalman.linear with parameters of type MatrixDecomposer

Constructor	Description
LinearKalmanFilter(MatrixDecomposer decomposer, LinearProcess<T> process, ProcessEstimate initialState)	Simple constructor.

Uses of MatrixDecomposer in org.hipparchus.filtering.kalman.unscented

Constructors in org.hipparchus.filtering.kalman.unscented with parameters of type MatrixDecomposer

Constructor	Description
UnscentedKalmanFilter(MatrixDecomposer decomposer, UnscentedProcess<T> process, ProcessEstimate initialState, UnscentedTransformProvider utProvider)	Simple constructor.

Uses of MatrixDecomposer in org.hipparchus.linear

Classes in org.hipparchus.linear that implement MatrixDecomposer

Modifier and Type	Class	Description
class	CholeskyDecomposer	Matrix decomposer using Cholseky decomposition.
class	LUDecomposer	Matrix decomposer using LU-decomposition.
class	QRDecomposer	Matrix decomposer using QR-decomposition.
class	SingularValueDecomposer	Matrix decomposer using Singular Value Decomposition.

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

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

Modifier and Type	Method	Description
MatrixDecomposer	GaussNewtonOptimizer.getDecomposer()	Get the matrix decomposition algorithm.
MatrixDecomposer	SequentialGaussNewtonOptimizer.getDecomposer()	Get the matrix decomposition algorithm.

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

Modifier and Type	Method	Description
GaussNewtonOptimizer	GaussNewtonOptimizer.withDecomposer(MatrixDecomposer newDecomposer)	Configure the matrix decomposition algorithm.
SequentialGaussNewtonOptimizer	SequentialGaussNewtonOptimizer.withDecomposer(MatrixDecomposer newDecomposer)	Configure the matrix decomposition algorithm.

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

Constructor	Description
GaussNewtonOptimizer(MatrixDecomposer decomposer, boolean formNormalEquations)	Create a Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.
SequentialGaussNewtonOptimizer(MatrixDecomposer decomposer, boolean formNormalEquations, LeastSquaresProblem.Evaluation evaluation)	Create a sequential Gauss Newton optimizer that uses the given matrix decomposition algorithm to solve the normal equations.