Uses of Interface
org.hipparchus.analysis.MultivariateVectorFunction
Packages that use MultivariateVectorFunction
Package
Description
Parent package for common numerical analysis procedures, including root finding,
function interpolation and integration.
This package holds the main interfaces and basic building block classes
dealing with differentiation.
Classes to perform curve fitting.
Algorithms for optimizing a scalar function.
This package provides algorithms that minimize the residuals
between observations and model values.
This package provides algorithms that minimize the residuals
between observations and model values.
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Uses of MultivariateVectorFunction in org.hipparchus.analysis
Methods in org.hipparchus.analysis with parameters of type MultivariateVectorFunctionModifier and TypeMethodDescriptionFunctionUtils.toDifferentiable
(MultivariateFunction f, MultivariateVectorFunction gradient) Convert regular functions toMultivariateDifferentiableFunction
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Uses of MultivariateVectorFunction in org.hipparchus.analysis.differentiation
Subinterfaces of MultivariateVectorFunction in org.hipparchus.analysis.differentiationModifier and TypeInterfaceDescriptioninterface
Extension ofMultivariateVectorFunction
representing a multivariate differentiable vectorial function.Classes in org.hipparchus.analysis.differentiation that implement MultivariateVectorFunctionModifier and TypeClassDescriptionclass
Class representing the gradient of a multivariate function. -
Uses of MultivariateVectorFunction in org.hipparchus.fitting
Methods in org.hipparchus.fitting that return MultivariateVectorFunctionModifier and TypeMethodDescriptionAbstractCurveFitter.TheoreticalValuesFunction.getModelFunction()
Get model function value. -
Uses of MultivariateVectorFunction in org.hipparchus.optim.nonlinear.scalar
Methods in org.hipparchus.optim.nonlinear.scalar that return MultivariateVectorFunctionModifier and TypeMethodDescriptionObjectiveFunctionGradient.getObjectiveFunctionGradient()
Gets the gradient of the function to be optimized.Constructors in org.hipparchus.optim.nonlinear.scalar with parameters of type MultivariateVectorFunctionModifierConstructorDescriptionLeastSquaresConverter
(MultivariateVectorFunction function, double[] observations) Builds a simple converter for uncorrelated residuals with identical weights.LeastSquaresConverter
(MultivariateVectorFunction function, double[] observations, double[] weights) Builds a simple converter for uncorrelated residuals with the specified weights.LeastSquaresConverter
(MultivariateVectorFunction function, double[] observations, RealMatrix scale) Builds a simple converter for correlated residuals with the specified weights.Simple constructor. -
Uses of MultivariateVectorFunction in org.hipparchus.optim.nonlinear.vector.constrained
Subinterfaces of MultivariateVectorFunction in org.hipparchus.optim.nonlinear.vector.constrainedModifier and TypeInterfaceDescriptioninterface
Generic constraint.interface
A MultivariateFunction that also has a defined gradient and Hessian.Classes in org.hipparchus.optim.nonlinear.vector.constrained that implement MultivariateVectorFunctionModifier and TypeClassDescriptionclass
Constraint with lower and upper bounds: \(l \le f(x) \le u\).class
Equality Constraint.class
Inequality Constraint with lower bound only: \(l \le f(x)\).class
A set of linear inequality constraints expressed as ub>Ax>lb.class
A set of linear equality constraints given as Ax = b.class
Set of linear inequality constraints expressed as \( A x \gt B\). -
Uses of MultivariateVectorFunction in org.hipparchus.optim.nonlinear.vector.leastsquares
Methods in org.hipparchus.optim.nonlinear.vector.leastsquares with parameters of type MultivariateVectorFunctionModifier and TypeMethodDescriptionstatic LeastSquaresProblem
LeastSquaresFactory.create
(MultivariateVectorFunction model, MultivariateMatrixFunction jacobian, double[] observed, double[] start, RealMatrix weight, ConvergenceChecker<LeastSquaresProblem.Evaluation> checker, int maxEvaluations, int maxIterations) Create aLeastSquaresProblem
from the given elements.LeastSquaresBuilder.model
(MultivariateVectorFunction value, MultivariateMatrixFunction jacobian) Configure the model function.static MultivariateJacobianFunction
LeastSquaresFactory.model
(MultivariateVectorFunction value, MultivariateMatrixFunction jacobian) Combine aMultivariateVectorFunction
with aMultivariateMatrixFunction
to produce aMultivariateJacobianFunction
.