swh:1:snp:af87cd67498ef4fe47c76ed3e7caffe5b61facaf
Tip revision: 82fc2dc7e13477e717020c761e227ed4e3f7012e authored by Axel Naumann on 07 June 2022, 15:59:39 UTC
"Update ROOT version files to v6.26/04."
"Update ROOT version files to v6.26/04."
Tip revision: 82fc2dc
Minimization.C
/// \file
/// \ingroup tutorial_r
/// \notebook -nodraw
/// Example based in
/// http://root.cern.ch/root/html/tutorials/fit/NumericalMinimization.C.html
/// http://stat.ethz.ch/R-manual/R-devel/library/stats/html/optim.html
///
/// \macro_code
///
/// \author Omar Zapata
#include<TRInterface.h>
//in the next function the *double pointer must be changed by a TVectorD,
//because the pointer has no meaning in R enviroment.
Double_t RosenBrock(const TVectorD xx )
{
const Double_t x = xx[0];
const Double_t y = xx[1];
const Double_t tmp1 = y-x*x;
const Double_t tmp2 = 1-x;
return 100*tmp1*tmp1+tmp2*tmp2;
}
TVectorD RosenBrockGrad(const TVectorD xx )
{
const Double_t x = xx[0];
const Double_t y = xx[1];
TVectorD grad(2);
grad[0]=-400 * x * (y - x * x) - 2 * (1 - x);
grad[1]=200 * (y - x * x);
return grad;
}
void Minimization()
{
ROOT::R::TRInterface &r=ROOT::R::TRInterface::Instance();
//passsing RosenBrock function to R
r["RosenBrock"]=ROOT::R::TRFunctionExport(RosenBrock);
//passsing RosenBrockGrad function to R
r["RosenBrockGrad"]=ROOT::R::TRFunctionExport(RosenBrockGrad);
//the option "method" could be "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN","Brent"
//the option "control" lets you put some constraints like
//"maxit" The maximum number of iterations.
//"abstol" The absolute convergence tolerance.
r.Execute("result <- optim( c(0.01,0.01), RosenBrock,method='BFGS',control = list(maxit = 1000000) )");
//"reltol" Relative convergence tolerance.
//Getting results from R
TVectorD min=r.Eval("result$par");
std::cout.precision(8);
//printing results
std::cout<<"-----------------------------------------"<<std::endl;
std::cout<<"Minimum x="<<min[0]<<" y="<<min[1]<<std::endl;
std::cout<<"Value at minimum ="<<RosenBrock(min)<<std::endl;
//using the gradient
r.Execute("optimHess(result$par, RosenBrock, RosenBrockGrad)");
r.Execute("hresult <- optim(c(-1.2,1), RosenBrock, NULL, method = 'BFGS', hessian = TRUE)");
//getting the min calculated with the gradient
TVectorD hmin=r.Eval("hresult$par");
//printing results
std::cout<<"-----------------------------------------"<<std::endl;
std::cout<<"Minimization with the Gradient"<<std::endl;
std::cout<<"Minimum x="<<hmin[0]<<" y="<<hmin[1]<<std::endl;
std::cout<<"Value at minimum ="<<RosenBrock(hmin)<<std::endl;
}