GSLSimAnMinimizer class for minimization using simulated annealing using the algorithm from GSL.

It implements the ROOT::Minimizer interface and a plug-in (name "GSLSimAn") exists to instantiate this class via the plug-in manager Configuration (Setting/getting) the options is done through the methods defined in the ROOT::Math::Minimizer class. The user needs to call the base class method ROOT::Math::Minimizer::SetOptions to set the corresponding options. Here is some code example for increasing n_tries from 200 (default) to 1000

ROOT::Math::GenAlgoOptions simanOpt;
simanOpt.SetValue("n_tries", 1000);
ROOT::Math::MinimizerOptions opt;
opt.SetExtraOptions(simanOpt);
minimizer->SetOptions(opt);

Definition at line 75 of file GSLSimAnMinimizer.h.

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

virtual	~GSLSimAnMinimizer ()
	Destructor (no operations)

virtual bool	Minimize ()
	method to perform the minimization

const GSLSimAnParams &	MinimizerParameters () const
	Get current minimizer option parameteres.

unsigned int	NCalls () const
	number of calls

void	SetParameters (const GSLSimAnParams &params)
	set new minimizer option parameters using directly the GSLSimAnParams structure

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 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

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.

virtual double	CovMatrix (unsigned int ivar, unsigned int jvar) const
	return covariance matrices element for variables ivar,jvar if the variable is fixed the return value is zero The ordering of the variables is the same as in the parameter and errors vectors

virtual int	CovMatrixStatus () const
	return status of covariance matrix using Minuit convention {0 not calculated 1 approximated 2 made pos def , 3 accurate} Minimizer who implements covariance matrix calculation will re-implement the method

virtual double	Edm () const
	return expected distance reached from the minimum (re-implement if minimizer provides it

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

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

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

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	ProvidesError () const
	minimizer provides error and error matrix

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

Protected Member Functions
void	DoSetMinimOptions (const GSLSimAnParams &params)
	Set the Minimizer options from the simulated annealing parameters.

void	DoSetSimAnParameters (const MinimizerOptions &opt)
	set minimizer option parameters from stored ROOT::Math::MinimizerOptions (fOpt)

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)

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

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

Private Attributes
ROOT::Math::GSLSimAnnealing	fSolver

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

int	fStatus

bool	fValidError

#include <Math/GSLSimAnMinimizer.h>

Inheritance diagram for ROOT::Math::GSLSimAnMinimizer:

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Constructor & Destructor Documentation

◆ GSLSimAnMinimizer() [1/2]

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

Default constructor.

Definition at line 33 of file GSLSimAnMinimizer.cxx.

◆ ~GSLSimAnMinimizer()

ROOT::Math::GSLSimAnMinimizer::~GSLSimAnMinimizer ( )

virtual

Destructor (no operations)

Definition at line 53 of file GSLSimAnMinimizer.cxx.

◆ GSLSimAnMinimizer() [2/2]

ROOT::Math::GSLSimAnMinimizer::GSLSimAnMinimizer ( const GSLSimAnMinimizer & )

inlineprivate

Copy constructor.

Definition at line 94 of file GSLSimAnMinimizer.h.

Member Function Documentation

◆ DoSetMinimOptions()

void ROOT::Math::GSLSimAnMinimizer::DoSetMinimOptions ( const GSLSimAnParams & params )

protected

Set the Minimizer options from the simulated annealing parameters.

Definition at line 153 of file GSLSimAnMinimizer.cxx.

◆ DoSetSimAnParameters()

void ROOT::Math::GSLSimAnMinimizer::DoSetSimAnParameters ( const MinimizerOptions & opt )

protected

set minimizer option parameters from stored ROOT::Math::MinimizerOptions (fOpt)

Definition at line 169 of file GSLSimAnMinimizer.cxx.

◆ Minimize()

bool ROOT::Math::GSLSimAnMinimizer::Minimize ( )

virtual

method to perform the minimization

Reimplemented from ROOT::Math::BasicMinimizer.

Definition at line 57 of file GSLSimAnMinimizer.cxx.

◆ MinimizerParameters()

const GSLSimAnParams & ROOT::Math::GSLSimAnMinimizer::MinimizerParameters ( ) const

inline

Get current minimizer option parameteres.

Definition at line 114 of file GSLSimAnMinimizer.h.

◆ NCalls()

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

virtual

number of calls

Reimplemented from ROOT::Math::Minimizer.

Definition at line 141 of file GSLSimAnMinimizer.cxx.

◆ operator=()

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

inlineprivate

Assignment operator.

Definition at line 99 of file GSLSimAnMinimizer.h.

◆ SetParameters()

void ROOT::Math::GSLSimAnMinimizer::SetParameters ( const GSLSimAnParams & params )

inline

set new minimizer option parameters using directly the GSLSimAnParams structure

Definition at line 117 of file GSLSimAnMinimizer.h.

Member Data Documentation

◆ fSolver

ROOT::Math::GSLSimAnnealing ROOT::Math::GSLSimAnMinimizer::fSolver

private

Definition at line 131 of file GSLSimAnMinimizer.h.

Libraries for ROOT::Math::GSLSimAnMinimizer:

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

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

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ GSLSimAnMinimizer() [1/2]

◆ ~GSLSimAnMinimizer()

◆ GSLSimAnMinimizer() [2/2]

Member Function Documentation

◆ DoSetMinimOptions()

◆ DoSetSimAnParameters()

◆ Minimize()

◆ MinimizerParameters()

◆ NCalls()

◆ operator=()

◆ SetParameters()

Member Data Documentation

◆ fSolver