TMinuitMinimizer class implementing the ROOT::Math::Minimizer interface using TMinuit. This class is normally instantiates using the plug-in manager (plug-in with name Minuit or TMinuit) In addition the user can choose the minimizer algorithm: Migrad (the default one), Simplex, or Minimize (combined Migrad + Simplex)
TMinuitMinimizer(ROOT::Minuit::EMinimizerType type = ROOT::Minuit::kMigrad) | |
TMinuitMinimizer(const char* type) | |
virtual | ~TMinuitMinimizer() |
static TClass* | Class() |
virtual void | ROOT::Math::Minimizer::Clear() |
virtual bool | Contour(unsigned int i, unsigned int j, unsigned int& npoints, double* xi, double* xj) |
virtual double | ROOT::Math::Minimizer::Correlation(unsigned int i, unsigned int j) const |
virtual double | CovMatrix(unsigned int i, unsigned int j) const |
virtual double | Edm() const |
double | ROOT::Math::Minimizer::ErrorDef() const |
virtual const double* | Errors() const |
virtual bool | GetMinosError(unsigned int i, double& errLow, double& errUp) |
virtual double | ROOT::Math::Minimizer::GlobalCC(unsigned int) const |
virtual TClass* | IsA() const |
bool | ROOT::Math::Minimizer::IsValidError() const |
unsigned int | ROOT::Math::Minimizer::MaxFunctionCalls() |
unsigned int | ROOT::Math::Minimizer::MaxIterations() |
virtual const double* | MinGradient() const |
virtual bool | Minimize() |
virtual double | MinValue() const |
virtual unsigned int | NCalls() const |
virtual unsigned int | NDim() const |
virtual unsigned int | NFree() const |
int | ROOT::Math::Minimizer::PrintLevel() const |
virtual void | PrintResults() |
virtual bool | ProvidesError() const |
virtual bool | Scan(unsigned int i, unsigned int& nstep, double* x, double* y, double xmin = 0, double xmax = 0) |
void | ROOT::Math::Minimizer::SetErrorDef(double up) |
virtual bool | SetFixedVariable(unsigned int, const string&, double) |
virtual void | SetFunction(const ROOT::Math::IMultiGenFunction& func) |
virtual void | SetFunction(const ROOT::Math::IMultiGradFunction& func) |
virtual bool | SetLimitedVariable(unsigned int ivar, const string& name, double val, double step, double, double) |
virtual bool | ROOT::Math::Minimizer::SetLowerLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower) |
void | ROOT::Math::Minimizer::SetMaxFunctionCalls(unsigned int maxfcn) |
void | ROOT::Math::Minimizer::SetMaxIterations(unsigned int maxiter) |
void | ROOT::Math::Minimizer::SetPrintLevel(int level) |
void | ROOT::Math::Minimizer::SetStrategy(int strategyLevel) |
void | ROOT::Math::Minimizer::SetTolerance(double tol) |
virtual bool | ROOT::Math::Minimizer::SetUpperLimitedVariable(unsigned int ivar, const string& name, double val, double step, double upper) |
void | ROOT::Math::Minimizer::SetValidError(bool on) |
virtual bool | SetVariable(unsigned int ivar, const string& name, double val, double step) |
virtual bool | SetVariableValue(unsigned int, double) |
virtual bool | ROOT::Math::Minimizer::SetVariableValues(const double* x) |
virtual void | ShowMembers(TMemberInspector& insp, char* parent) |
int | ROOT::Math::Minimizer::Status() const |
int | ROOT::Math::Minimizer::Strategy() const |
virtual void | Streamer(TBuffer& b) |
void | StreamerNVirtual(TBuffer& b) |
double | ROOT::Math::Minimizer::Tolerance() const |
virtual const double* | X() const |
void | DoClear() |
static void | Fcn(int&, double*, double& f, double*, int) |
static void | FcnGrad(int&, double* g, double& f, double*, int) |
TMinuitMinimizer(const TMinuitMinimizer&) | |
TMinuitMinimizer& | operator=(const TMinuitMinimizer& rhs) |
int | ROOT::Math::Minimizer::fDebug | print level |
unsigned int | ROOT::Math::Minimizer::fMaxCalls | max number of funciton calls |
unsigned int | ROOT::Math::Minimizer::fMaxIter | max number or iterations used to find the minimum |
int | ROOT::Math::Minimizer::fStatus | status of minimizer |
int | ROOT::Math::Minimizer::fStrategy | minimizer strategy |
double | ROOT::Math::Minimizer::fTol | tolerance (absolute) |
double | ROOT::Math::Minimizer::fUp | error scale |
bool | ROOT::Math::Minimizer::fValidError | flag to control if errors have been validated (Hesse has been run in case of Minuit) |
vector<double> | fCovar | |
unsigned int | fDim | |
double | fEdm | |
vector<double> | fErrors | |
double | fMinVal | |
bool | fMinosRun | |
TMinuit* | fMinuit | |
unsigned int | fNFree | |
vector<double> | fParams | |
unsigned int | fStrategy | |
ROOT::Minuit::EMinimizerType | fType | |
bool | fUsed | |
static ROOT::Math::IBaseFunctionMultiDim* | fgFunc | |
static TMinuit* | fgMinuit | |
static bool | fgUsed | flag to control if static instance has done minimization |
Constructor for TMinuitMinimier class via an enumeration specifying the minimization algorithm type. Supported types are : kMigrad, kSimplex, kCombined (a combined Migrad + Simplex minimization) and kMigradImproved (a Migrad mininimization folloed by an improved search for global minima). The default type is Migrad (kMigrad).
Set the objective function to be minimized, by passing a function object implement the basic multi-dim Function interface. In this case the derivatives will be calculated by Minuit
Set the objective function to be minimized, by passing a function object implement the multi-dim gradient Function interface. In this case the function derivatives are provided by the user via this interface and there not calculated by Minuit.
set a free variable.
set a limited variable.
set the value of an existing variable parameter must exist or return false
perform the minimization using the algorithm chosen previously by the user By default Migrad is used. Return true if the found minimum is valid and update internal chached values of minimum values, errors and covariance matrix. Status of minimizer is set to: migradResult + 10*minosResult + 100*hesseResult + 1000*improveResult
Perform Minos analysis for the given parameter i
contour plot for parameter i and j need a valid FuncitonMinimum otherwise exits
scan a parameter (variable) around the minimum value the parameters must have been set before if xmin=0 && xmax == 0 by default scan around 2 sigma of the error if the errors are also zero then scan from min and max of parameter range (if parameters are limited Minuit scan from min and max instead of 2 sigma by default) (force in that case to use errors)
this is <= Function().NDim() which is the total number of variables (free+ constrained ones)
{ return fDim; }
number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total
{ return fNFree; }
return covariance matrices elements if the variable is fixed the matrix is zero The ordering of the variables is the same as in errors