Minuit2Minimizer.h

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// @(#)root/minuit2:$Id$
// Author: L. Moneta Wed Oct 18 11:48:00 2006
 
/**********************************************************************
 *                                                                    *
 * Copyright (c) 2006  LCG ROOT Math Team, CERN/PH-SFT                *
 *                                                                    *
 *                                                                    *
 **********************************************************************/
 
// Header file for class Minuit2Minimizer
 
#ifndef ROOT_Minuit2_Minuit2Minimizer
#define ROOT_Minuit2_Minuit2Minimizer
 
#include "Math/Minimizer.h"
 
#include "Minuit2/MnUserParameterState.h"
 
#include "Math/IFunctionfwd.h"
 
#include <vector>
#include <string>
#include <functional>
 
namespace ROOT {
 
namespace Minuit2 {
 
class ModularFunctionMinimizer;
class FCNBase;
class FunctionMinimum;
class MnTraceObject;
 
// enumeration specifying the type of Minuit2 minimizers
enum EMinimizerType { kMigrad, kSimplex, kCombined, kScan, kFumili, kMigradBFGS };
 
} // namespace Minuit2
 
namespace Minuit2 {
//_____________________________________________________________________________________________________
/**
   Minuit2Minimizer class implementing the ROOT::Math::Minimizer interface for
   Minuit2 minimization algorithm.
   In ROOT it can be instantiated using the plug-in manager (plug-in "Minuit2")
   Using a string  (used by the plugin manager) or via an enumeration
   an one can set all the possible minimization algorithms (Migrad, Simplex, Combined, Scan and Fumili).
 
   Refer to the [guide](https://root.cern.ch/doc/master/Minuit2Page.html) for an introduction how Minuit2
   works.
 
   \note Some gradient calculations, such as `Numerical2PGradientCalculator` support parallelization
   via OpenMP. To profit from this acceleration, one needs to build ROOT using `minuit2_omp=ON`
   and later call GradientCalculator::SetParallelOMP()
 
   @ingroup Minuit
*/
class Minuit2Minimizer : public ROOT::Math::Minimizer {
 
public:
   /**
      Default constructor
   */
   Minuit2Minimizer(ROOT::Minuit2::EMinimizerType type = ROOT::Minuit2::kMigrad);
 
   /**
      Constructor with a char (used by PM)
   */
   Minuit2Minimizer(const char *type);
 
   /**
      Destructor (no operations)
   */
   ~Minuit2Minimizer() override;
 
   // clear resources (parameters) for consecutives minimizations
   void Clear() override;
 
   /// set the function to minimize
   void SetFunction(const ROOT::Math::IMultiGenFunction &func) override;
 
   /// set the function implementing Hessian computation
   void SetHessianFunction(std::function<bool(std::span<const double>, double *)> hfunc) override;
 
   /// set free variable
   bool SetVariable(unsigned int ivar, const std::string &name, double val, double step) override;
 
   /// set lower limit variable  (override if minimizer supports them )
   bool
   SetLowerLimitedVariable(unsigned int ivar, const std::string &name, double val, double step, double lower) override;
   /// set upper limit variable (override if minimizer supports them )
   bool
   SetUpperLimitedVariable(unsigned int ivar, const std::string &name, double val, double step, double upper) override;
   /// set upper/lower limited variable (override if minimizer supports them )
   bool SetLimitedVariable(unsigned int ivar, const std::string &name, double val, double step,
                                   double /* lower */, double /* upper */) override;
   /// set fixed variable (override if minimizer supports them )
   bool SetFixedVariable(unsigned int /* ivar */, const std::string & /* name */, double /* val */) override;
   /// set variable
   bool SetVariableValue(unsigned int ivar, double val) override;
   // set variable values
   bool SetVariableValues(const double *val) override;
   /// set the step size of an already existing variable
   bool SetVariableStepSize(unsigned int ivar, double step) override;
   /// set the lower-limit of an already existing variable
   bool SetVariableLowerLimit(unsigned int ivar, double lower) override;
   /// set the upper-limit of an already existing variable
   bool SetVariableUpperLimit(unsigned int ivar, double upper) override;
   /// set the limits of an already existing variable
   bool SetVariableLimits(unsigned int ivar, double lower, double upper) override;
   /// fix an existing variable
   bool FixVariable(unsigned int ivar) override;
   /// release an existing variable
   bool ReleaseVariable(unsigned int ivar) override;
   /// query if an existing variable is fixed (i.e. considered constant in the minimization)
   /// note that by default all variables are not fixed
   bool IsFixedVariable(unsigned int ivar) const override;
   /// get variable settings in a variable object (like ROOT::Fit::ParamsSettings)
   bool GetVariableSettings(unsigned int ivar, ROOT::Fit::ParameterSettings &varObj) const override;
   /// get name of variables (override if minimizer support storing of variable names)
   std::string VariableName(unsigned int ivar) const override;
   /// get index of variable given a variable given a name
   /// return -1 if variable is not found
   int VariableIndex(const std::string &name) const override;
 
   /**
       method to perform the minimization.
       Return false in case the minimization did not converge. In this case a
       status code different than zero is set
       (retrieved by the derived method Minimizer::Status() )"
 
       status = 1    : Covariance was made pos defined
       status = 2    : Hesse is invalid
       status = 3    : Edm is above max
       status = 4    : Reached call limit
       status = 5    : Any other failure
   */
   bool Minimize() override;
 
   /// return minimum function value
   double MinValue() const override { return fState.Fval(); }
 
   /// return expected distance reached from the minimum
   double Edm() const override { return fState.Edm(); }
 
   /// return  pointer to X values at the minimum
   const double *X() const override { return &fValues.front(); }
 
   /// return pointer to gradient values at the minimum
   const double *MinGradient() const override { return nullptr; } // not available in Minuit2
 
   /// number of function calls to reach the minimum
   unsigned int NCalls() const override { return fState.NFcn(); }
 
   /// this is <= Function().NDim() which is the total
   /// number of variables (free+ constrained ones)
   unsigned int NDim() const override { return fDim; }
 
   /// number of free variables (real dimension of the problem)
   /// this is <= Function().NDim() which is the total
   unsigned int NFree() const override { return fState.VariableParameters(); }
 
   /// minimizer provides error and error matrix
   bool ProvidesError() const override { return true; }
 
   /// return errors at the minimum
   const double *Errors() const override;
 
   /**
       return covariance matrix elements
       if the variable is fixed or const the value is zero
       The ordering of the variables is the same as in errors and parameter value.
       This is different from the direct interface of Minuit2 or TMinuit where the
       values were obtained only to variable parameters
   */
   double CovMatrix(unsigned int i, unsigned int j) const override;
 
   /**
       Fill the passed array with the  covariance matrix elements
       if the variable is fixed or const the value is zero.
       The array will be filled as cov[i *ndim + j]
       The ordering of the variables is the same as in errors and parameter value.
       This is different from the direct interface of Minuit2 or TMinuit where the
       values were obtained only to variable parameters
   */
   bool GetCovMatrix(double *cov) const override;
 
   /**
       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.
       The array will be filled as h[i *ndim + j]
   */
   bool GetHessianMatrix(double *h) const override;
 
   /**
      return the status of the covariance matrix
       status = -1 :  not available (inversion failed or Hesse failed)
       status =  0 : available but not positive defined
       status =  1 : covariance only approximate
       status =  2 : full matrix but forced pos def
       status =  3 : full accurate matrix
 
    */
   int CovMatrixStatus() const override;
   /**
      return correlation coefficient between variable i and j.
      If the variable is fixed or const the return value is zero
    */
   double Correlation(unsigned int i, unsigned int j) const override;
 
   /**
      get global correlation coefficient for the variable i. This is a number between zero and one which gives
      the correlation between the i-th variable  and that linear combination of all other variables which
      is most strongly correlated with i.
      If the variable is fixed or const the return value is zero
    */
   std::vector<double> GlobalCC() const override;
 
   /**
      get the minos error for parameter i, return false if Minos failed
      A minimizaiton must be performed befre, return false if no minimization has been done
      In case of Minos failed the status error is updated as following
      status += 10 * minosStatus.
      The Minos status of last Minos run can also be retrieved by calling MinosStatus()
   */
   bool GetMinosError(unsigned int i, double &errLow, double &errUp, int = 0) override;
 
   /**
      MINOS status code of last Minos run
       `status & 1 > 0`  : invalid lower error
       `status & 2 > 0`  : invalid upper error
       `status & 4 > 0`  : invalid because maximum number of function calls exceeded
       `status & 8 > 0`  : a new minimum has been found
       `status & 16 > 0` : error is truncated because parameter is at lower/upper limit
   */
   int MinosStatus() const override { return fMinosStatus; }
 
   /**
      scan a parameter i around the minimum. A minimization must have been done before,
      return false if it is not the case
    */
   bool Scan(unsigned int i, unsigned int &nstep, double *x, double *y, double xmin = 0, double xmax = 0) override;
 
   /**
      find the contour points (xi,xj) of the function for parameter i and j around the minimum
      The contour will be find for value of the function = Min + ErrorUp();
    */
   bool Contour(unsigned int i, unsigned int j, unsigned int &npoints, double *xi, double *xj) override;
 
   /**
      perform a full calculation of the Hessian matrix for error calculation
      If a valid minimum exists the calculation is done on the minimum point otherwise is performed
      in the current set values of parameters
      Status code of minimizer is updated according to the following convention (in case Hesse failed)
      status += 100*hesseStatus where hesse status is:
      status = 1 : hesse failed
      status = 2 : matrix inversion failed
      status = 3 : matrix is not pos defined
    */
   bool Hesse() override;
 
   /// return reference to the objective function
   /// virtual const ROOT::Math::IGenFunction & Function() const;
 
   /// print result of minimization
   void PrintResults() override;
 
   /// set an object to trace operation for each iteration
   /// The object must be a (or inherit from) ROOT::Minuit2::MnTraceObject and implement operator() (int, const
   /// MinimumState & state)
   void SetTraceObject(MnTraceObject &obj);
 
   /// set storage level   = 1 : store all iteration states (default)
   ///                     = 0 : store only first and last state to save memory
   void SetStorageLevel(int level);
 
   /// return the minimizer state (containing values, step size , etc..)
   const ROOT::Minuit2::MnUserParameterState &State() { return fState; }
 
   /// To set the function directly to a Minuit 2 function.
   void SetFCN(unsigned int nDim, std::unique_ptr<ROOT::Minuit2::FCNBase> fcn);
 
   const ROOT::Minuit2::FCNBase *GetFCN() const { return fMinuitFCN.get(); }
   ROOT::Minuit2::FCNBase *GetFCN() { return fMinuitFCN.get(); }
 
protected:
   // protected function for accessing the internal Minuit2 object. Needed for derived classes
 
   virtual const ROOT::Minuit2::ModularFunctionMinimizer *GetMinimizer() const { return fMinimizer.get(); }
 
   void SetMinimizerType(ROOT::Minuit2::EMinimizerType type);
 
   /// examine the minimum result
   bool ExamineMinimum(const ROOT::Minuit2::FunctionMinimum &min);
 
   // internal function to compute Minos errors
   int RunMinosError(unsigned int i, double &errLow, double &errUp, int runopt);
 
   /// set initial second derivatives
   virtual bool SetCovarianceDiag(std::span<const double> d2, unsigned int n) override;
 
   /// set initial covariance matrix
   bool SetCovariance(std::span<const double> cov, unsigned int nrow) override;
 
private:
   unsigned int fDim; // dimension of the function to be minimized
   bool fUseFumili;
   int fMinosStatus = -1; // Minos status code
 
   ROOT::Minuit2::MnUserParameterState fState;
   std::unique_ptr<ROOT::Minuit2::ModularFunctionMinimizer> fMinimizer;
   std::unique_ptr<ROOT::Minuit2::FCNBase> fMinuitFCN;
   std::unique_ptr<ROOT::Minuit2::FunctionMinimum> fMinimum;
   mutable std::vector<double> fValues;
   mutable std::vector<double> fErrors;
};
 
} // namespace Minuit2
 
} // end namespace ROOT
 
#endif /* ROOT_Minuit2_Minuit2Minimizer */