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

Definition at line 148 of file GSLNLSMinimizer.h.

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

	~GSLNLSMinimizer ()
	Destructor (no operations)

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

virtual int	CovMatrixStatus () const
	return covariance matrix status

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

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

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

virtual bool	Minimize ()
	method to perform the minimization

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

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

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

virtual void	SetFunction (const ROOT::Math::IMultiGradFunction &func)
	set gradient the function to minimize

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

virtual	~BasicMinimizer ()
	Destructor.

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

virtual bool	GetVariableSettings (unsigned int ivar, ROOT::Fit::ParameterSettings &varObj) const
	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)

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

virtual double	MinValue () const
	return minimum function value

virtual unsigned int	NDim () const
	number of dimensions

virtual unsigned int	NFree () const
	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

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

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

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

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

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

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

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

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

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

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

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

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

virtual const double *	StepSizes () const
	accessor methods

const ROOT::Math::MinimTransformFunction *	TransformFunction () const
	return transformation function (NULL if not having a transformation)

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

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

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

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

virtual	~Minimizer ()
	Destructor (no operations)

virtual void	Clear ()
	reset for consecutive minimizations - 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

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.

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

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

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

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

Private Member Functions
	GSLNLSMinimizer (const GSLNLSMinimizer &)
	Copy constructor.

GSLNLSMinimizer &	operator= (const GSLNLSMinimizer &rhs)
	Assignment operator.

Private Attributes
const ROOT::Math::FitMethodFunction *	fChi2Func

std::vector< double >	fCovMatrix

double	fEdm

std::vector< double >	fErrors

ROOT::Math::GSLMultiFit *	fGSLMultiFit

double	fLSTolerance

unsigned int	fNFree

std::vector< LSResidualFunc >	fResiduals

unsigned int	fSize

Additional Inherited Members
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=0)

void	SetFinalValues (const double *x)

void	SetMinValue (double val)

Protected Attributes inherited from ROOT::Math::Minimizer
MinimizerOptions	fOptions

int	fStatus

bool	fValidError

#include <Math/GSLNLSMinimizer.h>

Inheritance diagram for ROOT::Math::GSLNLSMinimizer:

[legend]

Constructor & Destructor Documentation

◆ GSLNLSMinimizer() [1/2]

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

Default constructor.

Definition at line 136 of file GSLNLSMinimizer.cxx.

◆ ~GSLNLSMinimizer()

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

Destructor (no operations)

Definition at line 161 of file GSLNLSMinimizer.cxx.

◆ GSLNLSMinimizer() [2/2]

ROOT::Math::GSLNLSMinimizer::GSLNLSMinimizer ( const GSLNLSMinimizer & )

inlineprivate

Copy constructor.

Definition at line 168 of file GSLNLSMinimizer.h.

Member Function Documentation

◆ CovMatrix()

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

virtual

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 371 of file GSLNLSMinimizer.cxx.

◆ CovMatrixStatus()

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

virtual

return covariance matrix status

Reimplemented from ROOT::Math::Minimizer.

Definition at line 379 of file GSLNLSMinimizer.cxx.

◆ Edm()

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

inlinevirtual

return expected distance reached from the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 192 of file GSLNLSMinimizer.h.

◆ Errors()

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

inlinevirtual

return errors at the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 209 of file GSLNLSMinimizer.h.

◆ MinGradient()

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

virtual

return pointer to gradient values at the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 365 of file GSLNLSMinimizer.cxx.

◆ Minimize()

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

virtual

method to perform the minimization

Reimplemented from ROOT::Math::BasicMinimizer.

Definition at line 200 of file GSLNLSMinimizer.cxx.

◆ NCalls()

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

inlinevirtual

number of function calls to reach the minimum

Reimplemented from ROOT::Math::Minimizer.

Definition at line 199 of file GSLNLSMinimizer.h.

◆ operator=()

GSLNLSMinimizer & ROOT::Math::GSLNLSMinimizer::operator= ( const GSLNLSMinimizer & rhs )

inlineprivate

Assignment operator.

Definition at line 173 of file GSLNLSMinimizer.h.

◆ ProvidesError()

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

inlinevirtual

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 206 of file GSLNLSMinimizer.h.

◆ SetFunction() [1/2]

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

virtual

set the function to minimize

Reimplemented from ROOT::Math::BasicMinimizer.

Definition at line 168 of file GSLNLSMinimizer.cxx.

◆ SetFunction() [2/2]

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

virtual

set gradient the function to minimize

Reimplemented from ROOT::Math::BasicMinimizer.

Definition at line 193 of file GSLNLSMinimizer.cxx.

Member Data Documentation

◆ fChi2Func

const ROOT::Math::FitMethodFunction* ROOT::Math::GSLNLSMinimizer::fChi2Func

private

Definition at line 234 of file GSLNLSMinimizer.h.

◆ fCovMatrix

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

private

Definition at line 239 of file GSLNLSMinimizer.h.

◆ fEdm

double ROOT::Math::GSLNLSMinimizer::fEdm

private

Definition at line 236 of file GSLNLSMinimizer.h.

◆ fErrors

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

private

Definition at line 238 of file GSLNLSMinimizer.h.

◆ fGSLMultiFit

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

private

Definition at line 233 of file GSLNLSMinimizer.h.

◆ fLSTolerance

double ROOT::Math::GSLNLSMinimizer::fLSTolerance

private

Definition at line 237 of file GSLNLSMinimizer.h.

◆ fNFree

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

private

Definition at line 230 of file GSLNLSMinimizer.h.

◆ fResiduals

std::vector<LSResidualFunc> ROOT::Math::GSLNLSMinimizer::fResiduals

private

Definition at line 240 of file GSLNLSMinimizer.h.

◆ fSize

unsigned int ROOT::Math::GSLNLSMinimizer::fSize

private

Definition at line 231 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

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ GSLNLSMinimizer() [1/2]

◆ ~GSLNLSMinimizer()

◆ GSLNLSMinimizer() [2/2]

Member Function Documentation

◆ CovMatrix()

◆ CovMatrixStatus()

◆ Edm()

◆ Errors()

◆ MinGradient()

◆ Minimize()

◆ NCalls()

◆ operator=()

◆ ProvidesError()

◆ SetFunction() [1/2]

◆ SetFunction() [2/2]

Member Data Documentation

◆ fChi2Func

◆ fCovMatrix

◆ fEdm

◆ fErrors

◆ fGSLMultiFit

◆ fLSTolerance

◆ fNFree

◆ fResiduals

◆ fSize