gross-pitaevskii/example_02.cpp

Go to the documentation of this file.

// @HEADER
// ************************************************************************
//
//               Rapid Optimization Library (ROL) Package
//                 Copyright (2014) Sandia Corporation
//
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact lead developers:
//              Drew Kouri   (dpkouri@sandia.gov) and
//              Denis Ridzal (dridzal@sandia.gov)
//
// ************************************************************************
// @HEADER

#include<algorithm>
#include<string>
#include"example_02.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);
    bool   exactsolve = gplist.get("Solve Exact Augmented System",false);

    // Command line option to override parameters.xml for solving the exact augmented system
    if(argc > 1) {
        std::string input = argv[1];
        std::transform(input.begin(), input.end(), input.begin(), ::tolower);  
        if(input=="exactsolve") {
            exactsolve = true;
        }
    }

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

    // Finite difference class
    Teuchos::RCP<FiniteDifference<RealT> > fd = Teuchos::rcp(new FiniteDifference<RealT>(nx,dx));

    // 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) );
    OptStdVector<RealT> psi(psi_rcp,fd);
       
    // 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]);
    }


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

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

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

    // Instantiate objective function  
    Objective_GrossPitaevskii<RealT,OptStdVector<RealT>,OptDualStdVector<RealT> > obj(gnl,V,fd);
    
    // Instantiate normalization constraint
    Normalization_Constraint<RealT,OptStdVector<RealT>,OptDualStdVector<RealT>, 
             ConStdVector<RealT>,ConDualStdVector<RealT> > constr(nx,dx,fd,exactsolve);


    // 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;
}