Quasi-Newton Method with trust-region

We solve the Rosenbrock problem using a Quasi-Newton method with BFGS update for the Hessian. The implemented trust-region method is the dogleg method. Other valid globalization strategies are line-search and trust region parallel direct search method. An iteration history is written to file tstqnewton.out.

   //
   // Test program for Quasi-Newton optimization objects
   //

   #include <fstream>

   #include "OptQNewton.h"
   #include "NLF.h"
   #include "tstfcn.h"

   using NEWMAT::ColumnVector;
   using namespace OPTPP;

   void update_model(int, int, ColumnVector) {}

   int main ()
   {
     int n = 2;
  
     static char *status_file = {"tstqnewton.out"};


     //  Create a Nonlinear problem object
     NLF1 nlp(n,rosen,init_rosen);
  
     nlp.setIsExpensive(true);

     //  Create a "Tolerances" object and set the tolerances
     TOLS tol;         
     tol.setDefaultTol();
     tol.setFTol(1.e-9);    // Set convergence tolerance to 1.e-9 
     tol.setMaxIter(200);   // Set maximum number of outer iterations to 200
  
     //  Build a Quasi-Newton object and optimize 
     OptQNewton objfcn(&nlp,tol);   

     if (!objfcn.setOutputFile(status_file, 0))
       cerr << "main: output file open failed" << endl;

     objfcn.setTRSize(100.); // Set initial trust region radius to 100
     objfcn.optimize();

     objfcn.printStatus("Solution from quasi-newton");
     objfcn.cleanup();   

   }

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Last revised September 14, 2006 .

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