operator-maxwell.h

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/*
  This file is part of MADNESS.
  
  Copyright (C) 2007,2010 Oak Ridge National Laboratory
  
  This program is free software; you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation; either version 2 of the License, or
  (at your option) any later version.
  
  This program is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  GNU General Public License for more details.
  
  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
  
  For more information please contact:
  
  Robert J. Harrison
  Oak Ridge National Laboratory
  One Bethel Valley Road
  P.O. Box 2008, MS-6367
  
  email: harrisonrj@ornl.gov
  tel:   865-241-3937
  fax:   865-572-0680
  
  $Id$
*/
#ifndef __operator_maxwell__
#define __operator_maxwell__

#include <string>
#include <madness/constants.h>
#include <madness/mra/mra.h>
#include <madness/tensor/gmres.h>

using namespace std;
using namespace madness;

typedef double_complex complexd;
typedef Function<complexd, 3> function;
typedef Vector<function, 3> vecfunc;

class EFieldOperator : public Operator<vecfunc> {
protected:
        const function &epshat;
        const complexd prefact;
        const vecfunc &gradlnepshat;
        const Function<double, 3> &box_mask; // mask to make the boundary 0
        const Function<double, 3> &grad_mask; // mask to damp out derivatives
        const SeparatedConvolution<double, 3> &G;

        void action(const vecfunc &in, vecfunc &out) const {
                function dotp = (gradlnepshat[0]*in[0] + gradlnepshat[1]*in[1]
                        + gradlnepshat[2]*in[2]) * box_mask;
                vecfunc preop;
                int i;

                for(i = 0; i < 3; ++i)
                        preop[i] = diff(dotp, i) * grad_mask;
                dotp.clear();

                for(i = 0; i < 3; ++i) {
                        dotp = epshat*in[i];
                        dotp.compress();
                        preop[i].compress();
                        preop[i].gaxpy(complexd(1.0, 0.0), dotp, prefact);
                        out[i] = in[i] - apply(G, preop[i]);
                        out[i].truncate();
                        dotp.clear();
                        preop[i].clear();
                }
        }

public:
        EFieldOperator(const function &_epshat, const double _omega,
                const double _mu0, const double _eps0, const vecfunc &_gradlnepshat,
                const Function<double, 3> &_box_mask,
                const Function<double, 3> &_grad_mask,
                const SeparatedConvolution<double, 3> &_G) : epshat(_epshat),
                prefact(complexd(0.0, -_omega*_eps0*_mu0)), gradlnepshat(_gradlnepshat),
                box_mask(_box_mask), grad_mask(_grad_mask), G(_G) {}
};

#endif