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class ROOT::Math::GSLNLSMinimizer: public ROOT::Math::Minimizer

    LSResidualFunc class description.
    Internal class used for accessing the residuals of the Least Square function
    and their derivates which are estimated numerically using GSL numerical derivation.
    The class contains a pointer to the fit method function and an index specifying
    the i-th residual and wraps it in a multi-dim gradient function interface
    The class is used by ROOT::Math::GSLNLSMinimizer (GSL non linear least square fitter)

    @ingroup MultiMin

Function Members (Methods)

virtual voidROOT::Math::Minimizer::Clear()
virtual boolROOT::Math::Minimizer::Contour(unsigned int, unsigned int, unsigned int&, double*, double*)
virtual doubleROOT::Math::Minimizer::Correlation(unsigned int i, unsigned int j) const
virtual doubleCovMatrix(unsigned int, unsigned int) const
virtual intCovMatrixStatus() const
virtual doubleEdm() const
doubleROOT::Math::Minimizer::ErrorDef() const
virtual const double*Errors() const
virtual boolROOT::Math::Minimizer::GetMinosError(unsigned int, double& errLow, double& errUp, int = 0)
virtual doubleROOT::Math::Minimizer::GlobalCC(unsigned int) const
ROOT::Math::GSLNLSMinimizerGSLNLSMinimizer(int type = 0)
virtual boolROOT::Math::Minimizer::Hesse()
boolROOT::Math::Minimizer::IsValidError() const
unsigned intROOT::Math::Minimizer::MaxFunctionCalls()
unsigned intROOT::Math::Minimizer::MaxIterations()
virtual const double*MinGradient() const
virtual boolMinimize()
virtual doubleMinValue() const
virtual unsigned intNCalls() const
virtual unsigned intNDim() const
virtual unsigned intNFree() const
doubleROOT::Math::Minimizer::Precision() const
intROOT::Math::Minimizer::PrintLevel() const
virtual voidROOT::Math::Minimizer::PrintResults()
virtual boolProvidesError() const
virtual boolROOT::Math::Minimizer::Scan(unsigned int, unsigned int&, double*, double*, double = 0, double = 0)
voidROOT::Math::Minimizer::SetErrorDef(double up)
virtual boolSetFixedVariable(unsigned int ivar, const string& name, double val)
virtual voidSetFunction(const ROOT::Math::IMultiGenFunction& func)
virtual voidSetFunction(const ROOT::Math::IMultiGradFunction& func)
virtual boolSetLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower, double upper)
virtual boolSetLowerLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower)
voidROOT::Math::Minimizer::SetMaxFunctionCalls(unsigned int maxfcn)
voidROOT::Math::Minimizer::SetMaxIterations(unsigned int maxiter)
voidROOT::Math::Minimizer::SetPrecision(double prec)
voidROOT::Math::Minimizer::SetPrintLevel(int level)
voidROOT::Math::Minimizer::SetStrategy(int strategyLevel)
voidROOT::Math::Minimizer::SetTolerance(double tol)
virtual boolSetUpperLimitedVariable(unsigned int ivar, const string& name, double val, double step, double upper)
voidROOT::Math::Minimizer::SetValidError(bool on)
virtual boolSetVariable(unsigned int ivar, const string& name, double val, double step)
virtual boolSetVariableValue(unsigned int ivar, double val)
virtual boolSetVariableValues(const double* x)
intROOT::Math::Minimizer::Status() const
intROOT::Math::Minimizer::Strategy() const
doubleROOT::Math::Minimizer::Tolerance() const
virtual intROOT::Math::Minimizer::VariableIndex(const string&) const
virtual stringROOT::Math::Minimizer::VariableName(unsigned int) const
virtual const double*X() const

Data Members

intROOT::Math::Minimizer::fDebugprint level
unsigned intROOT::Math::Minimizer::fMaxCallsmax number of function calls
unsigned intROOT::Math::Minimizer::fMaxItermax number or iterations used to find the minimum
intROOT::Math::Minimizer::fStatusstatus of minimizer
intROOT::Math::Minimizer::fStrategyminimizer strategy
doubleROOT::Math::Minimizer::fToltolerance (absolute)
doubleROOT::Math::Minimizer::fUperror scale
boolROOT::Math::Minimizer::fValidErrorflag to control if errors have been validated (Hesse has been run in case of Minuit)
map<unsigned int,std::pair<double,double> >fBoundsmap specifying the bound using as key the parameter index
vector<double>fCovMatrixcov matrix (stored as cov[ i * dim + j]
unsigned intfDimdimension of the function to be minimized
doublefEdmedm value
ROOT::Math::GSLMultiFit*fGSLMultiFitpointer to GSL multi fit solver
doublefLSToleranceLine Search Tolerance
doublefMinValminimum function value
unsigned intfNFreedimension of the internal function to be minimized
const ROOT::Math::FitMethodFunction*fObjFuncpointer to Least square function
vector<LSResidualFunc>fResiduals! transient Vector of the residual functions
unsigned intfSizenumber of fit points (residuals)
vector<ROOT::Math::EMinimVariableType>fVarTypesvector specifyng the type of variables

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

operator=(const ROOT::Math::GSLNLSMinimizer& rhs)
unsigned int NDim() const
{ return fChi2->NDim(); }
GSLNLSMinimizer(int type = 0)
      Default constructor

      Destructor (no operations)

GSLNLSMinimizer(const GSLNLSMinimizer &)
 usually copying is non trivial, so we make this unaccessible

      Copy constructor

void SetFunction(const ROOT::Math::IMultiGenFunction & func)
 set the function to minimize
void SetFunction(const ROOT::Math::IMultiGradFunction & func)
 set gradient the function to minimize
bool SetVariable(unsigned int ivar, const string& name, double val, double step)
 set free variable
bool SetLowerLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower)
 set lower limited variable
bool SetUpperLimitedVariable(unsigned int ivar, const string& name, double val, double step, double upper)
 set upper limited variable
bool SetLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower, double upper)
 set upper/lower limited variable
bool SetFixedVariable(unsigned int ivar, const string& name, double val)
 set fixed variable
bool SetVariableValue(unsigned int ivar, double val)
 set the value of an existing variable
bool SetVariableValues(const double* x)
 set the values of all existing variables (array must be dimensioned to the size of existing parameters)
bool Minimize()
 method to perform the minimization
double MinValue() const
 return minimum function value
{ return fMinVal; }
double Edm() const
 return expected distance reached from the minimum
const double * X() const
 return  pointer to X values at the minimum
{ return &fValues.front(); }
const double * MinGradient() const
 return pointer to gradient values at the minimum
unsigned int NCalls() const
 number of function calls to reach the minimum
{ return 0; }
unsigned int NFree() const
 number of free variables (real dimension of the problem)
 this is <= Function().NDim() which is the total
{ return fNFree; }
bool ProvidesError() const
 minimizer provides error and error matrix
{ return true; }
const double * Errors() const
 return errors at the minimum
{ return (fErrors.size() > 0) ? &fErrors.front() : 0; }
double CovMatrix(unsigned int , unsigned int ) const
       static std::vector<double> err;
       return &err.front();
 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
 return covariance matrix status