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ROOT::Math::GSLNLSMinimizer Class Reference

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 doubleErrors () const
return errors at the minimum

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)

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::IMultiGenFunctionObjFunction () 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 doubleStepSizes () const
accessor methods

const ROOT::Math::MinimTransformFunctionTransformFunction () 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 doubleX () 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 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)

int Status () const
status code of minimizer

int Strategy () const
strategy

double Tolerance () const
absolute tolerance

## Private Member Functions

GSLNLSMinimizer (const GSLNLSMinimizer &)
Copy constructor.

GSLNLSMinimizeroperator= (const GSLNLSMinimizer &rhs)
Assignment operator.

## Private Attributes

const ROOT::Math::FitMethodFunctionfChi2Func

std::vector< doublefCovMatrix

double fEdm

std::vector< doublefErrors

ROOT::Math::GSLMultiFitfGSLMultiFit

double fLSTolerance

unsigned int fNFree

std::vector< LSResidualFuncfResiduals

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

bool CheckObjFunction () const

MinimTransformFunctionCreateTransformation (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]

## ◆ 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.

## ◆ 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.

 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.

## ◆ fChi2Func

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

Definition at line 234 of file GSLNLSMinimizer.h.

## ◆ fCovMatrix

 std::vector 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 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 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: [legend]

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