Uses of Interface
org.ddogleg.optimization.functions.FunctionNtoN

Packages that use FunctionNtoN

Package	Description
org.ddogleg.optimization
org.ddogleg.optimization.derivative
org.ddogleg.optimization.loss
org.ddogleg.optimization.trustregion
org.ddogleg.optimization.wrap

Uses of FunctionNtoN in org.ddogleg.optimization

Methods in org.ddogleg.optimization that return FunctionNtoN

Modifier and Type	Method	Description
static FunctionNtoN	FactoryNumericalDerivative.gradientForwards(FunctionNtoS func)

Methods in org.ddogleg.optimization with parameters of type FunctionNtoN

Modifier and Type	Method	Description
static boolean	DerivativeChecker.gradient(FunctionNtoS func, FunctionNtoN gradient, double[] param, double tol)	Compares the passed in gradient function to a numerical calculation.
static boolean	DerivativeChecker.gradient(FunctionNtoS func, FunctionNtoN gradient, double[] param, double tol, double differenceScale)
static boolean	DerivativeChecker.gradientR(FunctionNtoS func, FunctionNtoN gradient, double[] param, double tol)	Compares the passed in gradient function to a numerical calculation.
static boolean	DerivativeChecker.gradientR(FunctionNtoS func, FunctionNtoN gradient, double[] param, double tol, double differenceScale)
void	UnconstrainedMinimization.setFunction(FunctionNtoS function, FunctionNtoN gradient, double minFunctionValue)	Specifies the function being optimized.

Uses of FunctionNtoN in org.ddogleg.optimization.derivative

Classes in org.ddogleg.optimization.derivative that implement FunctionNtoN

Modifier and Type	Class	Description
class	NumericalGradientFB	Finite difference numerical gradient calculation using the forward+backwards equation.
class	NumericalGradientForward	Finite difference numerical gradient calculation using forward equation.

Uses of FunctionNtoN in org.ddogleg.optimization.loss

Subinterfaces of FunctionNtoN in org.ddogleg.optimization.loss

Modifier and Type	Interface	Description
interface	LossFunctionGradient	Analytical gradient for a function that implements LossFunction.

Classes in org.ddogleg.optimization.loss that implement FunctionNtoN

Modifier and Type	Class	Description
static class	LossCauchy.Gradient	Implementation of the smooth Cauchy loss gradient
static class	LossHuber.Gradient	Implementation of the Huber Loss gradient
static class	LossHuberSmooth.Gradient	Implementation of the smooth Huber loss gradient
static class	LossIdentity.Gradient
class	LossIRLS	Iteratively Reweighted Least-Squares (IRLS) allows the weights to be recomputed every iteration.
static class	LossSquared.Gradient
static class	LossTukey.Gradient	Implementation of the Tukey Loss gradient
class	LossWeighted	A weighted least squares cost function.

Uses of FunctionNtoN in org.ddogleg.optimization.trustregion

Methods in org.ddogleg.optimization.trustregion with parameters of type FunctionNtoN

Modifier and Type	Method	Description
void	UnconMinTrustRegionBFGS_F64.setFunction(FunctionNtoS function, FunctionNtoN gradient, double minFunctionValue)

Uses of FunctionNtoN in org.ddogleg.optimization.wrap

Classes in org.ddogleg.optimization.wrap that implement FunctionNtoN

Modifier and Type	Class	Description
class	LsToNonLinearDeriv<S extends DMatrix>	Convert the Jacobian of a least squares function into a nonlinear optimization gradient.

Fields in org.ddogleg.optimization.wrap declared as FunctionNtoN

Modifier and Type	Field	Description
protected FunctionNtoN	CachedGradientLineFunction.gradient
protected FunctionNtoN	CachedNumericalGradientLineFunction.gradient

Methods in org.ddogleg.optimization.wrap with parameters of type FunctionNtoN

Modifier and Type	Method	Description
void	QuasiNewtonBFGS_to_UnconstrainedMinimization.setFunction(FunctionNtoS function, FunctionNtoN gradient, double minFunctionValue)

Constructors in org.ddogleg.optimization.wrap with parameters of type FunctionNtoN

Modifier	Constructor	Description
	CachedGradientLineFunction(FunctionNtoS function, FunctionNtoN gradient)