GSLNLSMinimizer class for Non Linear Least Square fitting It Uses the Levemberg-Marquardt algorithm from GSL Non Linear Least Square fitting.

Definition at line 59 of file GSLNLSMinimizer.h.

Public Member Functions
	GSLNLSMinimizer (int type=0)
	Default constructor.

	~GSLNLSMinimizer () override
	Destructor (no operations)

double	CovMatrix (unsigned int, unsigned int) const override
	return covariance matrices elements if the variable is fixed the matrix is zero The ordering of the variables is the same as in errors

int	CovMatrixStatus () const override
	return covariance matrix status

double	Edm () const override
	return expected distance reached from the minimum

const double *	Errors () const override
	return errors at the minimum

const double *	MinGradient () const override
	return pointer to gradient values at the minimum

bool	Minimize () override
	method to perform the minimization

unsigned int	NCalls () const override
	number of function calls to reach the minimum

bool	ProvidesError () const override
	number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total

void	SetFunction (const ROOT::Math::IMultiGenFunction &func) override
	set the function to minimize

Public Member Functions inherited from ROOT::Math::BasicMinimizer
	BasicMinimizer ()
	Default constructor.

	~BasicMinimizer () override
	Destructor.

bool	FixVariable (unsigned int ivar) override
	fix an existing variable

bool	GetVariableSettings (unsigned int ivar, ROOT::Fit::ParameterSettings &varObj) const override
	get variable settings in a variable object (like ROOT::Fit::ParamsSettings)

const ROOT::Math::IMultiGradFunction *	GradObjFunction () const
	return pointer to used gradient object function (NULL if gradient is not supported)

bool	IsFixedVariable (unsigned int ivar) const override
	query if an existing variable is fixed (i.e.

double	MinValue () const override
	return minimum function value

unsigned int	NDim () const override
	number of dimensions

unsigned int	NFree () const override
	number of free variables (real dimension of the problem)

virtual unsigned int	NPar () const
	total number of parameter defined

const ROOT::Math::IMultiGenFunction *	ObjFunction () const
	return pointer to used objective function

void	PrintResult () const
	print result of minimization

bool	ReleaseVariable (unsigned int ivar) override
	release an existing variable

bool	SetFixedVariable (unsigned int, const std::string &, double) override
	set fixed variable (override if minimizer supports them )

bool	SetLimitedVariable (unsigned int ivar, const std::string &name, double val, double step, double, double) override
	set upper/lower limited variable (override if minimizer supports them )

bool	SetLowerLimitedVariable (unsigned int ivar, const std::string &name, double val, double step, double lower) override
	set lower limit variable (override if minimizer supports them )

bool	SetUpperLimitedVariable (unsigned int ivar, const std::string &name, double val, double step, double upper) override
	set upper limit variable (override if minimizer supports them )

bool	SetVariable (unsigned int ivar, const std::string &name, double val, double step) override
	set free variable

bool	SetVariableLimits (unsigned int ivar, double lower, double upper) override
	set the limits of an already existing variable

bool	SetVariableLowerLimit (unsigned int ivar, double lower) override
	set the lower-limit of an already existing variable

bool	SetVariableStepSize (unsigned int ivar, double step) override
	set the step size of an already existing variable

bool	SetVariableUpperLimit (unsigned int ivar, double upper) override
	set the upper-limit of an already existing variable

bool	SetVariableValue (unsigned int ivar, double val) override
	set the value of an existing variable

bool	SetVariableValues (const double *x) override
	set the values of all existing variables (array must be dimensioned to the size of existing parameters)

virtual const double *	StepSizes () const
	accessor methods

int	VariableIndex (const std::string &name) const override
	get index of variable given a variable given a name return -1 if variable is not found

std::string	VariableName (unsigned int ivar) const override
	get name of variables (override if minimizer support storing of variable names)

const double *	X () const override
	return pointer to X values at the minimum

Public Member Functions inherited from ROOT::Math::Minimizer
	Minimizer ()
	Default constructor.

	Minimizer (Minimizer &&)=delete

	Minimizer (Minimizer const &)=delete

virtual	~Minimizer ()
	Destructor (no operations).

virtual void	Clear ()
	reset for consecutive minimization - implement if needed

virtual bool	Contour (unsigned int ivar, unsigned int jvar, unsigned int &npoints, double xi, double xj)
	find the contour points (xi, xj) of the function for parameter ivar and jvar around the minimum The contour will be find for value of the function = Min + ErrorUp();

virtual double	Correlation (unsigned int i, unsigned int j) const
	return correlation coefficient between variable i and j.

double	ErrorDef () const
	return the statistical scale used for calculate the error is typically 1 for Chi2 and 0.5 for likelihood minimization

virtual bool	GetCovMatrix (double *covMat) const
	Fill the passed array with the covariance matrix elements if the variable is fixed or const the value is zero.

virtual bool	GetHessianMatrix (double *hMat) const
	Fill the passed array with the Hessian matrix elements The Hessian matrix is the matrix of the second derivatives and is the inverse of the covariance matrix If the variable is fixed or const the values for that variables are zero.

virtual bool	GetMinosError (unsigned int ivar, double &errLow, double &errUp, int option=0)
	minos error for variable i, return false if Minos failed or not supported and the lower and upper errors are returned in errLow and errUp An extra flag specifies if only the lower (option=-1) or the upper (option=+1) error calculation is run

virtual double	GlobalCC (unsigned int ivar) const
	return global correlation coefficient for variable i This is a number between zero and one which gives the correlation between the i-th parameter and that linear combination of all other parameters which is most strongly correlated with i.

virtual bool	Hesse ()
	perform a full calculation of the Hessian matrix for error calculation

bool	IsValidError () const
	return true if Minimizer has performed a detailed error validation (e.g. run Hesse for Minuit)

unsigned int	MaxFunctionCalls () const
	max number of function calls

unsigned int	MaxIterations () const
	max iterations

virtual int	MinosStatus () const
	status code of Minos (to be re-implemented by the minimizers supporting Minos)

virtual unsigned int	NIterations () const
	number of iterations to reach the minimum

Minimizer &	operator= (Minimizer &&)=delete

Minimizer &	operator= (Minimizer const &)=delete

virtual MinimizerOptions	Options () const
	retrieve the minimizer options (implement derived class if needed)

double	Precision () const
	precision of minimizer in the evaluation of the objective function ( a value <=0 corresponds to the let the minimizer choose its default one)

int	PrintLevel () const
	minimizer configuration parameters

virtual void	PrintResults ()
	return reference to the objective function virtual const ROOT::Math::IGenFunction & Function() const = 0;

virtual bool	Scan (unsigned int ivar, unsigned int &nstep, double x, double y, double xmin=0, double xmax=0)
	scan function minimum for variable i.

virtual bool	SetCovariance (std::span< const double > cov, unsigned int nrow)
	set initial covariance matrix

virtual bool	SetCovarianceDiag (std::span< const double > d2, unsigned int n)
	set initial second derivatives

void	SetDefaultOptions ()
	reset the default options (defined in MinimizerOptions)

void	SetErrorDef (double up)
	set scale for calculating the errors

void	SetExtraOptions (const IOptions &extraOptions)
	set only the extra options

virtual void	SetHessianFunction (std::function< bool(std::span< const double >, double *)>)
	set the function implementing Hessian computation (re-implemented by Minimizer using it)

void	SetMaxFunctionCalls (unsigned int maxfcn)
	set maximum of function calls

void	SetMaxIterations (unsigned int maxiter)
	set maximum iterations (one iteration can have many function calls)

void	SetOptions (const MinimizerOptions &opt)
	set all options in one go

void	SetPrecision (double prec)
	set in the minimizer the objective function evaluation precision ( a value <=0 means the minimizer will choose its optimal value automatically, i.e.

void	SetPrintLevel (int level)
	set print level

void	SetStrategy (int strategyLevel)
	set the strategy

void	SetTolerance (double tol)
	set the tolerance

void	SetValidError (bool on)
	flag to check if minimizer needs to perform accurate error analysis (e.g. run Hesse for Minuit)

virtual bool	SetVariableInitialRange (unsigned int, double, double)
	set the initial range of an existing variable

template<class VariableIterator >
int	SetVariables (const VariableIterator &begin, const VariableIterator &end)
	add variables . Return number of variables successfully added

int	Status () const
	status code of minimizer

int	Strategy () const
	strategy

double	Tolerance () const
	absolute tolerance

Protected Member Functions
template<class Func >
bool	DoMinimize (const Func &f)
	Internal method to perform minimization template on the type of method function.

Protected Member Functions inherited from ROOT::Math::BasicMinimizer
bool	CheckDimension () const

bool	CheckObjFunction () const

MinimTransformFunction *	CreateTransformation (std::vector< double > &startValues, const ROOT::Math::IMultiGradFunction *func=nullptr)

void	SetFinalValues (const double x, const MinimTransformFunction func=nullptr)

void	SetMinValue (double val)

Private Attributes
std::vector< double >	fCovMatrix

double	fEdm

std::vector< double >	fErrors

ROOT::Math::GSLMultiFit *	fGSLMultiFit

double	fLSTolerance

unsigned int	fNCalls

unsigned int	fNFree

bool	fUseGradFunction = false

Additional Inherited Members
Protected Attributes inherited from ROOT::Math::Minimizer
MinimizerOptions	fOptions
	minimizer options

int	fStatus = -1
	status of minimizer

bool	fValidError = false
	flag to control if errors have been validated (Hesse has been run in case of Minuit)

#include <Math/GSLNLSMinimizer.h>

Inheritance diagram for ROOT::Math::GSLNLSMinimizer:

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Constructor & Destructor Documentation

◆ GSLNLSMinimizer()

ROOT::Math::GSLNLSMinimizer::GSLNLSMinimizer ( int type = 0 )

Default constructor.

Definition at line 206 of file GSLNLSMinimizer.cxx.

◆ ~GSLNLSMinimizer()

ROOT::Math::GSLNLSMinimizer::~GSLNLSMinimizer ( )

override

Destructor (no operations)

Definition at line 228 of file GSLNLSMinimizer.cxx.

Member Function Documentation

◆ CovMatrix()

double ROOT::Math::GSLNLSMinimizer::CovMatrix	(	unsigned int	i,
		unsigned int	j
	)		const

overridevirtual

return covariance matrices elements if the variable is fixed the matrix is zero The ordering of the variables is the same as in errors

Reimplemented from ROOT::Math::Minimizer.

Definition at line 456 of file GSLNLSMinimizer.cxx.

◆ CovMatrixStatus()

int ROOT::Math::GSLNLSMinimizer::CovMatrixStatus ( ) const

overridevirtual

return covariance matrix status

Reimplemented from ROOT::Math::Minimizer.

Definition at line 464 of file GSLNLSMinimizer.cxx.

◆ DoMinimize()

template<class Func >

bool ROOT::Math::GSLNLSMinimizer::DoMinimize ( const Func & f )

protected

Internal method to perform minimization template on the type of method function.

Definition at line 268 of file GSLNLSMinimizer.cxx.

◆ Edm()

double ROOT::Math::GSLNLSMinimizer::Edm ( ) const

inlineoverridevirtual

return expected distance reached from the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 82 of file GSLNLSMinimizer.h.

◆ Errors()

const double * ROOT::Math::GSLNLSMinimizer::Errors ( ) const

inlineoverridevirtual

return errors at the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 99 of file GSLNLSMinimizer.h.

◆ MinGradient()

const double * ROOT::Math::GSLNLSMinimizer::MinGradient ( ) const

overridevirtual

return pointer to gradient values at the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 450 of file GSLNLSMinimizer.cxx.

◆ Minimize()

bool ROOT::Math::GSLNLSMinimizer::Minimize ( )

overridevirtual

method to perform the minimization

Reimplemented from ROOT::Math::BasicMinimizer.

Definition at line 247 of file GSLNLSMinimizer.cxx.

◆ NCalls()

unsigned int ROOT::Math::GSLNLSMinimizer::NCalls ( ) const

inlineoverridevirtual

number of function calls to reach the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 89 of file GSLNLSMinimizer.h.

◆ ProvidesError()

bool ROOT::Math::GSLNLSMinimizer::ProvidesError ( ) const

inlineoverridevirtual

number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total

minimizer provides error and error matrix

Reimplemented from ROOT::Math::Minimizer.

Definition at line 96 of file GSLNLSMinimizer.h.

◆ SetFunction()

void ROOT::Math::GSLNLSMinimizer::SetFunction ( const ROOT::Math::IMultiGenFunction & func )

overridevirtual

set the function to minimize

Reimplemented from ROOT::Math::BasicMinimizer.

Definition at line 235 of file GSLNLSMinimizer.cxx.

Member Data Documentation

◆ fCovMatrix

std::vector<double> ROOT::Math::GSLNLSMinimizer::fCovMatrix

private

Definition at line 134 of file GSLNLSMinimizer.h.

◆ fEdm

double ROOT::Math::GSLNLSMinimizer::fEdm

private

Definition at line 131 of file GSLNLSMinimizer.h.

◆ fErrors

std::vector<double> ROOT::Math::GSLNLSMinimizer::fErrors

private

Definition at line 133 of file GSLNLSMinimizer.h.

◆ fGSLMultiFit

ROOT::Math::GSLMultiFit* ROOT::Math::GSLNLSMinimizer::fGSLMultiFit

private

Definition at line 129 of file GSLNLSMinimizer.h.

◆ fLSTolerance

double ROOT::Math::GSLNLSMinimizer::fLSTolerance

private

Definition at line 132 of file GSLNLSMinimizer.h.

◆ fNCalls

unsigned int ROOT::Math::GSLNLSMinimizer::fNCalls

private

Definition at line 127 of file GSLNLSMinimizer.h.

◆ fNFree

unsigned int ROOT::Math::GSLNLSMinimizer::fNFree

private

Definition at line 126 of file GSLNLSMinimizer.h.

◆ fUseGradFunction

bool ROOT::Math::GSLNLSMinimizer::fUseGradFunction = false

private

Definition at line 125 of file GSLNLSMinimizer.h.

Libraries for ROOT::Math::GSLNLSMinimizer:

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

math/mathmore/inc/Math/GSLNLSMinimizer.h
math/mathmore/src/GSLNLSMinimizer.cxx

Public Member Functions

Protected Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ GSLNLSMinimizer()

◆ ~GSLNLSMinimizer()

Member Function Documentation

◆ CovMatrix()

◆ CovMatrixStatus()

◆ DoMinimize()

◆ Edm()

◆ Errors()

◆ MinGradient()

◆ Minimize()

◆ NCalls()

◆ ProvidesError()

◆ SetFunction()

Member Data Documentation

◆ fCovMatrix

◆ fEdm

◆ fErrors

◆ fGSLMultiFit

◆ fLSTolerance

◆ fNCalls

◆ fNFree

◆ fUseGradFunction