Abstract Minimizer class, defining the interface for the various minimizer (like Minuit2, Minuit, GSL, etc..) Plug-in's exist in ROOT to be able to instantiate the derived classes like ROOT::Math::GSLMinimizer or ROOT::Math::Minuit2Minimizer via the plug-in manager. Provides interface for setting the function to be minimized. The function must implemente the multi-dimensional generic interface ROOT::Math::IBaseFunctionMultiDim. If the function provides gradient calculation (implements the ROOT::Math::IGradientFunctionMultiDim interface) this will be used by the Minimizer. It Defines also interface for setting the initial values for the function variables (which are the parameters in of the model function in case of solving for fitting) and especifying their limits. It defines the interface to set and retrieve basic minimization parameters (for specific Minimizer parameters one must use the derived classes). Then it defines the interface to retrieve the result of minimization ( minimum X values, function value, gradient, error on the mimnimum, etc...) @ingroup MultiMin
| virtual | ~Minimizer() |
| virtual void | Clear() |
| virtual double | Correlation(unsigned int i, unsigned int j) const |
| virtual double | CovMatrix(unsigned int i, unsigned int j) const |
| virtual double | Edm() const |
| virtual const double* | Errors() const |
| double | ErrorUp() const |
| virtual bool | GetMinosError(unsigned int, double& errLow, double& errUp) |
| bool | IsValidError() const |
| unsigned int | MaxFunctionCalls() |
| unsigned int | 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 | PrintLevel() const |
| virtual bool | ProvidesError() const |
| void | SetErrorUp(double up) |
| virtual bool | SetFixedVariable(unsigned int ivar, const string& name, double val) |
| 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 | SetLowerLimitedVariable(unsigned int ivar, const string& name, double val, double step, double lower) |
| void | SetMaxFunctionCalls(unsigned int maxfcn) |
| void | SetMaxIterations(unsigned int maxiter) |
| void | SetPrintLevel(int level) |
| void | SetStrategy(int strategyLevel) |
| void | SetTolerance(double tol) |
| virtual bool | SetUpperLimitedVariable(unsigned int ivar, const string& name, double val, double step, double upper) |
| void | SetValidError(bool on) |
| virtual bool | SetVariable(unsigned int ivar, const string& name, double val, double step) |
| int | Strategy() const |
| double | Tolerance() const |
| virtual const double* | X() const |
set a function to minimize using gradient
set upper limit variable (override if minimizer supports them )
set fixed variable (override if minimizer supports them )
this is <= Function().NDim() which is the total number of variables (free+ constrained ones)
number of free variables (real dimension of the problem) this is <= Function().NDim() which is the total
return covariance matrices elements
if the variable is fixed the matrix is zero
The ordering of the variables is the same as in errors
return correlation coefficient between variable i and j.
If the variable is fixed or const the return value is zero
minos error for variable i, return false if Minos failed or not supported
return reference to the objective function virtual const ROOT::Math::IGenFunction & Function() const = 0; minimizer configuration parameters set print level
{ return fDebug; }return the statistical scale used for calculate the error is typically 1 for Chi2 minimizetion and 0.5 for likelihood's
{ return fUp; }return true if Minimizer has performed a detailed error validation (e.g. run Hesse for Minuit)
{ return fValidError; }set maximum of function calls
{ if (maxfcn > 0) fMaxCalls = maxfcn; }set maximum iterations (one iteration can have many function calls)
{ if (maxiter > 0) fMaxIter = maxiter; }flag to check if minimizer needs to perform accurate error analysis (e.g. run Hesse for Minuit)
{ fValidError = on; }