Adapter class to wrap user-provided functions into the FCNBase interface.

This class allows users to supply their function (and optionally its gradient, diagonal second derivatives, and Hessian) in the form of std::function objects. It adapts these functions so that they can be used transparently with the MINUIT minimizers via the FCNBase interface.

Typical usage:

Pass the function to minimize to the constructor.
Optionally set the gradient, G2 (second derivative diagonal), or Hessian functions using the provided setter methods.
MINUIT will then query these functions if available, or fall back to numerical approximations if they are not provided.

Definition at line 37 of file FCNAdapter.h.

Public Member Functions
	FCNAdapter (std::function< double(double const *)> f, double up=1.)
	Construct an adapter around a user-provided function.
virtual double	ErrorDef () const
	Error definition of the function.
std::vector< double >	G2 (std::vector< double > const &x) const override
	Return the diagonal elements of the Hessian (second derivatives).
std::vector< double >	Gradient (std::vector< double > const &v) const override
	Evaluate the gradient of the function at the given parameter vector.
virtual std::vector< double >	GradientWithPrevResult (std::vector< double > const &parameters, double , double , double *) const
virtual GradientParameterSpace	gradParameterSpace () const
bool	HasG2 () const override
	Indicate whether analytic second derivatives (diagonal of the Hessian) are available.
bool	HasGradient () const override
	Indicate whether an analytic gradient has been provided.
bool	HasHessian () const override
	Indicate whether an analytic Hessian has been provided.
std::vector< double >	Hessian (std::vector< double > const &x) const override
	Return the full Hessian matrix.
double	operator() (std::vector< double > const &v) const override
	Evaluate the function at the given parameter vector.
void	SetErrorDef (double up) override
	Update the error definition parameter.
void	SetG2Function (std::function< std::vector< double >(std::vector< double > const &)> f)
	Set the function providing diagonal second derivatives (G2).
void	SetGradientFunction (std::function< void(double const , double )> f)
	Set the analytic gradient function.
void	SetHessianFunction (std::function< bool(std::vector< double > const &, double *)> f)
	Set the function providing the full Hessian matrix.
double	Up () const override
	Return the error definition parameter (up).

Private Types
using	Function = std::function<double(double const *)>
using	G2Function = std::function<std::vector<double>(std::vector<double> const &)>
using	GradFunction = std::function<void(double const , double )>
using	HessianFunction = std::function<bool(std::vector<double> const &, double *)>

Private Attributes
Function	fFunc
	Wrapped function to minimize.
G2Function	fG2Func
	Optional diagonal second-derivative function.
GradFunction	fGradFunc
	Optional gradient function.
std::vector< double >	fHessian
	Storage for intermediate Hessian values.
HessianFunction	fHessianFunc
	Optional Hessian function.
double	fUp = 1.
	Error definition parameter.

#include <Minuit2/FCNAdapter.h>

Inheritance diagram for ROOT::Minuit2::FCNAdapter:

Member Typedef Documentation

◆ Function

using ROOT::Minuit2::FCNAdapter::Function = std::function<double(double const *)>

private

Definition at line 164 of file FCNAdapter.h.

◆ G2Function

using ROOT::Minuit2::FCNAdapter::G2Function = std::function<std::vector<double>(std::vector<double> const &)>

private

Definition at line 166 of file FCNAdapter.h.

◆ GradFunction

using ROOT::Minuit2::FCNAdapter::GradFunction = std::function<void(double const *, double *)>

private

Definition at line 165 of file FCNAdapter.h.

◆ HessianFunction

using ROOT::Minuit2::FCNAdapter::HessianFunction = std::function<bool(std::vector<double> const &, double *)>

private

Definition at line 167 of file FCNAdapter.h.

Constructor & Destructor Documentation

◆ FCNAdapter()

ROOT::Minuit2::FCNAdapter::FCNAdapter	(	std::function< double(double const *)>	f,
		double	up = 1. )

inline

Construct an adapter around a user-provided function.

Parameters

f	Function to minimize. It must take a pointer to the parameter array (double const*) and return the function value.
up	Error definition parameter (defaults to 1.0).

Definition at line 45 of file FCNAdapter.h.

Member Function Documentation

◆ ErrorDef()

virtual double ROOT::Minuit2::FCNBase::ErrorDef ( ) const

inlinevirtualinherited

Error definition of the function.

MINUIT defines Parameter errors as the change in Parameter Value required to change the function Value by up. Normally, for chisquared fits it is 1, and for negative log likelihood, its Value is 0.5. If the user wants instead the 2-sigma errors for chisquared fits, it becomes 4, as Chi2(x+n*sigma) = Chi2(x) + n*n.

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Definition at line 70 of file FCNBase.h.

◆ G2()

std::vector< double > ROOT::Minuit2::FCNAdapter::G2 ( std::vector< double > const & x ) const

inlineoverridevirtual

Return the diagonal elements of the Hessian (second derivatives).

If a G2 function is set, it is used directly. If only a Hessian function is available, the diagonal is extracted from the full Hessian.

Parameters

x	Parameter vector.

Returns: Vector of second derivatives (one per parameter).

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 91 of file FCNAdapter.h.

◆ Gradient()

std::vector< double > ROOT::Minuit2::FCNAdapter::Gradient ( std::vector< double > const & v ) const

inlineoverridevirtual

Evaluate the gradient of the function at the given parameter vector.

Parameters

v	Parameter vector.

Returns: Gradient vector (∂f/∂xᵢ) at the specified parameters.

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 77 of file FCNAdapter.h.

◆ GradientWithPrevResult()

virtual std::vector< double > ROOT::Minuit2::FCNBase::GradientWithPrevResult	(	std::vector< double > const &	parameters,
		double *	,
		double *	,
		double *	) const

inlinevirtualinherited

Warning: Not meant to be overridden! This is a requirement for an internal optimization in RooFit that might go away with any refactoring.

Definition at line 99 of file FCNBase.h.

◆ gradParameterSpace()

virtual GradientParameterSpace ROOT::Minuit2::FCNBase::gradParameterSpace ( ) const

inlinevirtualinherited

Warning: Not meant to be overridden! This is a requirement for an internal optimization in RooFit that might go away with any refactoring.

Definition at line 107 of file FCNBase.h.

◆ HasG2()

bool ROOT::Minuit2::FCNAdapter::HasG2 ( ) const

inlineoverridevirtual

Indicate whether analytic second derivatives (diagonal of the Hessian) are available.

Returns: true if a G2 function or a Hessian function has been set, otherwise false.

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 55 of file FCNAdapter.h.

◆ HasGradient()

bool ROOT::Minuit2::FCNAdapter::HasGradient ( ) const

inlineoverridevirtual

Indicate whether an analytic gradient has been provided.

Returns: true if a gradient function was set, otherwise false.

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 50 of file FCNAdapter.h.

◆ HasHessian()

bool ROOT::Minuit2::FCNAdapter::HasHessian ( ) const

inlineoverridevirtual

Indicate whether an analytic Hessian has been provided.

Returns: true if a Hessian function was set, otherwise false.

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 60 of file FCNAdapter.h.

◆ Hessian()

std::vector< double > ROOT::Minuit2::FCNAdapter::Hessian ( std::vector< double > const & x ) const

inlineoverridevirtual

Return the full Hessian matrix.

If a Hessian function is available, it is used to fill the matrix. If the Hessian function fails, it is cleared and not used again.

Parameters

x	Parameter vector.

Returns: Flattened Hessian matrix in row-major order.

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 118 of file FCNAdapter.h.

◆ operator()()

double ROOT::Minuit2::FCNAdapter::operator() ( std::vector< double > const & v ) const

inlineoverridevirtual

Evaluate the function at the given parameter vector.

Parameters

v	Parameter vector.

Returns: Function value at the specified parameters.

Implements ROOT::Minuit2::FCNBase.

Definition at line 66 of file FCNAdapter.h.

◆ SetErrorDef()

void ROOT::Minuit2::FCNAdapter::SetErrorDef ( double up )

inlineoverridevirtual

Update the error definition parameter.

Parameters

up	New error definition value.

Reimplemented from ROOT::Minuit2::FCNBase.

Definition at line 161 of file FCNAdapter.h.

◆ SetG2Function()

void ROOT::Minuit2::FCNAdapter::SetG2Function ( std::function< std::vector< double >(std::vector< double > const &)> f )

inline

Set the function providing diagonal second derivatives (G2).

Parameters

f	Function taking a parameter vector and returning the diagonal of the Hessian matrix as a vector.

Definition at line 145 of file FCNAdapter.h.

◆ SetGradientFunction()

void ROOT::Minuit2::FCNAdapter::SetGradientFunction ( std::function< void(double const *, double *)> f )

inline

Set the analytic gradient function.

Parameters

f	Gradient function of type void(double const, double). The first argument is the parameter array, the second is the output array for the gradient values.

Definition at line 139 of file FCNAdapter.h.

◆ SetHessianFunction()

void ROOT::Minuit2::FCNAdapter::SetHessianFunction ( std::function< bool(std::vector< double > const &, double *)> f )

inline

Set the function providing the full Hessian matrix.

Parameters

f	Function of type bool(std::vector<double> const&, double*). The first argument is the parameter vector, the second is the output buffer (flattened matrix). The return value should be true on success, false on failure.

Definition at line 153 of file FCNAdapter.h.

◆ Up()

double ROOT::Minuit2::FCNAdapter::Up ( ) const

inlineoverridevirtual

Return the error definition parameter (up).

Returns: Current error definition value.

Implements ROOT::Minuit2::FCNBase.

Definition at line 71 of file FCNAdapter.h.

Member Data Documentation

◆ fFunc

Function ROOT::Minuit2::FCNAdapter::fFunc

private

Wrapped function to minimize.

Definition at line 172 of file FCNAdapter.h.

◆ fG2Func

G2Function ROOT::Minuit2::FCNAdapter::fG2Func

private

Optional diagonal second-derivative function.

Definition at line 174 of file FCNAdapter.h.

◆ fGradFunc

GradFunction ROOT::Minuit2::FCNAdapter::fGradFunc

private

Optional gradient function.

Definition at line 173 of file FCNAdapter.h.

◆ fHessian

std::vector<double> ROOT::Minuit2::FCNAdapter::fHessian

mutableprivate

Storage for intermediate Hessian values.

Definition at line 170 of file FCNAdapter.h.

◆ fHessianFunc

HessianFunction ROOT::Minuit2::FCNAdapter::fHessianFunc

mutableprivate

Optional Hessian function.

Definition at line 175 of file FCNAdapter.h.

◆ fUp

double ROOT::Minuit2::FCNAdapter::fUp = 1.

private

Error definition parameter.

Definition at line 169 of file FCNAdapter.h.

The documentation for this class was generated from the following file:

math/minuit2/inc/Minuit2/FCNAdapter.h

Public Member Functions

Private Types

Private Attributes

Member Typedef Documentation

◆ Function

◆ G2Function

◆ GradFunction

◆ HessianFunction

Constructor & Destructor Documentation

◆ FCNAdapter()

Member Function Documentation

◆ ErrorDef()

◆ G2()

◆ Gradient()

◆ GradientWithPrevResult()

◆ gradParameterSpace()

◆ HasG2()

◆ HasGradient()

◆ HasHessian()

◆ Hessian()

◆ operator()()

◆ SetErrorDef()

◆ SetG2Function()

◆ SetGradientFunction()

◆ SetHessianFunction()

◆ Up()

Member Data Documentation

◆ fFunc

◆ fG2Func

◆ fGradFunc

◆ fHessian

◆ fHessianFunc

◆ fUp