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

Packages that use FunctionNtoS

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

Uses of FunctionNtoS in org.ddogleg.optimization

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

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 FunctionNtoN	FactoryNumericalDerivative.gradientForwards(FunctionNtoS func)
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 FunctionNtoS in org.ddogleg.optimization.derivative

Constructors in org.ddogleg.optimization.derivative with parameters of type FunctionNtoS

Modifier	Constructor	Description
	NumericalGradientFB(FunctionNtoS function)
	NumericalGradientFB(FunctionNtoS function, double differenceScale)
	NumericalGradientForward(FunctionNtoS function)
	NumericalGradientForward(FunctionNtoS function, double differenceScale)

Uses of FunctionNtoS in org.ddogleg.optimization.loss

Subinterfaces of FunctionNtoS in org.ddogleg.optimization.loss

Modifier and Type	Interface	Description
interface	LossFunction	Residual loss function for regression.

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

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

Uses of FunctionNtoS in org.ddogleg.optimization.trustregion

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

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

Uses of FunctionNtoS in org.ddogleg.optimization.wrap

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

Modifier and Type	Class	Description
class	LsToNonLinear	Converts a least squares function into a nonlinear optimization function.

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

Modifier and Type	Field	Description
protected FunctionNtoS	CachedGradientLineFunction.function
protected FunctionNtoS	CachedNumericalGradientLineFunction.function

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

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 FunctionNtoS

Modifier	Constructor	Description
	CachedGradientLineFunction(FunctionNtoS function, FunctionNtoN gradient)
	CachedNumericalGradientLineFunction(FunctionNtoS function)