gross-pitaevskii/example_01.cpp

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#include "example_01.hpp"

typedef double RealT;

int main(int argc, char **argv) {

    // Set up MPI
    Teuchos::GlobalMPISession mpiSession(&argc, &argv);

    // This little trick lets us print to std::cout only if a (dummy) command-line argument is provided.
    int iprint     = argc - 1;
    Teuchos::RCP<std::ostream> outStream;
    Teuchos::oblackholestream bhs; // outputs nothing
    if (iprint > 0)
        outStream = Teuchos::rcp(&std::cout, false);
    else
        outStream = Teuchos::rcp(&bhs, false);

    int errorFlag = 0;

    Teuchos::ParameterList parlist;
    Teuchos::ParameterList gplist;

    std::string paramfile = "parameters.xml";
    Teuchos::updateParametersFromXmlFile(paramfile,Teuchos::Ptr<Teuchos::ParameterList>(&gplist));
 
    int nx    = gplist.get("Interior Grid Points",100);
    RealT gnl = gplist.get("Nonlinearity Coefficient g",50.0);

    // Grid spacing
    RealT dx = 1.0/(nx+1);

    // Pointer to linspace type vector \f$x_i = \frac{i+1}{n_x+1}\f$ where \f$i=0,\hdots,n_x\f$
    Teuchos::RCP<std::vector<RealT> > xi_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 0.0) );
    
    for(int i=0; i<nx; ++i) {
        (*xi_rcp)[i] = RealT(i+1)/(nx+1);
    }
    
    // Pointer to potential vector (quadratic centered at x=0.5)
    Teuchos::RCP<std::vector<RealT> > V_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 0.0) );
    for(int i=0; i<nx; ++i) {
       (*V_rcp)[i] = 100.0*pow((*xi_rcp)[i]-0.5,2);
    }

    StdVector<RealT> V(V_rcp);
        
    // Iteration Vector (pointer to optimzation vector)
    Teuchos::RCP<std::vector<RealT> > psi_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 0.0) );

       
    // Set Initial Guess (normalized)
    RealT sqrt30 = sqrt(30);

    for (int i=0; i<nx; i++) {
        (*psi_rcp)[i]   = sqrt30*(*xi_rcp)[i]*(1.0-(*xi_rcp)[i]);
    }

    StdVector<RealT> psi(psi_rcp);

    // Equality constraint value (scalar)  
    Teuchos::RCP<std::vector<RealT> > c_rcp = Teuchos::rcp( new std::vector<RealT> (1, 0.0) );
    StdVector<RealT> c(c_rcp);

    // Lagrange multiplier value (scalar)   
    Teuchos::RCP<std::vector<RealT> > lam_rcp = Teuchos::rcp( new std::vector<RealT> (1, 0.0) );
    StdVector<RealT> lam(lam_rcp);

    // Gradient   
    Teuchos::RCP<std::vector<RealT> > g_rcp = Teuchos::rcp( new std::vector<RealT> (nx, 0.0) );
    StdVector<RealT> g(g_rcp);

    // Instantiate objective function  
    Objective_GrossPitaevskii<RealT> obj(gnl,V);
    
    // Instantiate normalization constraint
    Normalization_Constraint<RealT> constr(nx,dx);

    // Define algorithm.
    std::string stepname = "Composite Step";
    parlist.sublist("Step").sublist(stepname).sublist("Optimality System Solver").set("Nominal Relative Tolerance",1e-4);
    parlist.sublist("Step").sublist(stepname).sublist("Optimality System Solver").set("Fix Tolerance",true);
    parlist.sublist("Step").sublist(stepname).sublist("Tangential Subproblem Solver").set("Iteration Limit",20);
    parlist.sublist("Step").sublist(stepname).sublist("Tangential Subproblem Solver").set("Relative Tolerance",1e-2);
    parlist.sublist("Step").sublist(stepname).set("Output Level",0);
    parlist.sublist("Status Test").set("Gradient Tolerance",1.e-12);
    parlist.sublist("Status Test").set("Constraint Tolerance",1.e-12);
    parlist.sublist("Status Test").set("Step Tolerance",1.e-14);
    parlist.sublist("Status Test").set("Iteration Limit",100);
    ROL::Algorithm<RealT> algo(stepname, parlist);

    // Run algorithm.
    algo.run(psi, g, lam, c, obj, constr, true, *outStream);


   if(algo.getState()->gnorm>1e-6) {
        errorFlag += 1; 
    }

    if (errorFlag != 0)
        std::cout << "End Result: TEST FAILED\n";
    else
        std::cout << "End Result: TEST PASSED\n";



    return 0;

}