MultiplyQualityMetric.cpp

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/* *****************************************************************
    MESQUITE -- The Mesh Quality Improvement Toolkit

    Copyright 2004 Sandia Corporation and Argonne National
    Laboratory.  Under the terms of Contract DE-AC04-94AL85000
    with Sandia Corporation, the U.S. Government retains certain
    rights in this software.

    This library is free software; you can redistribute it and/or
    modify it under the terms of the GNU Lesser General Public
    License as published by the Free Software Foundation; either
    version 2.1 of the License, or (at your option) any later version.

    This library 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
    Lesser General Public License for more details.

    You should have received a copy of the GNU Lesser General Public License
    (lgpl.txt) along with this library; if not, write to the Free Software
    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA

    [email protected], [email protected], [email protected],
    [email protected], [email protected], [email protected]

  ***************************************************************** */
/*!
  \file   MultiplyQualityMetric.cpp
  \brief

  \author Michael Brewer
  \date   2002-05-09
*/

#include "MultiplyQualityMetric.hpp"
#include "Vector3D.hpp"
#include "Matrix3D.hpp"
#include "MsqError.hpp"

using namespace MBMesquite;

MultiplyQualityMetric::MultiplyQualityMetric( QualityMetric* qm1, QualityMetric* qm2, MsqError& err )
    : metric1( *qm1 ), metric2( *qm2 )
{
    if( qm1->get_metric_type() != qm2->get_metric_type() || qm1->get_negate_flag() != qm2->get_negate_flag() )
    {
        MSQ_SETERR( err )( "Incompatible metrics", MsqError::INVALID_ARG );
    }
}

MultiplyQualityMetric::~MultiplyQualityMetric() {}

QualityMetric::MetricType MultiplyQualityMetric::get_metric_type() const
{
    return metric1.get_metric_type();
}

std::string MultiplyQualityMetric::get_name() const
{
    return metric1.get_name() + "*" + metric2.get_name();
}

int MultiplyQualityMetric::get_negate_flag() const
{
    return metric1.get_negate_flag();
}

void MultiplyQualityMetric::get_evaluations( PatchData& pd,
                                             std::vector< size_t >& handles,
                                             bool free_only,
                                             MsqError& err )
{
    metric1.get_evaluations( pd, handles, free_only, err );MSQ_ERRRTN( err );
    metric2.get_evaluations( pd, mHandles, free_only, err );MSQ_ERRRTN( err );
    if( handles != mHandles )
    {
        MSQ_SETERR( err )( "Incompatible metrics", MsqError::INVALID_STATE );
    }
}

bool MultiplyQualityMetric::evaluate( PatchData& pd, size_t handle, double& value, MsqError& err )
{
    double val1, val2;
    bool rval1, rval2;
    rval1 = metric1.evaluate( pd, handle, val1, err );
    MSQ_ERRZERO( err );
    rval2 = metric2.evaluate( pd, handle, val2, err );
    MSQ_ERRZERO( err );
    value = val1 * val2;
    return rval1 && rval2;
}

bool MultiplyQualityMetric::evaluate_with_indices( PatchData& pd,
                                                   size_t handle,
                                                   double& value,
                                                   std::vector< size_t >& indices,
                                                   MsqError& err )
{
    double val1, val2;
    bool rval1, rval2;
    rval1 = metric1.evaluate_with_indices( pd, handle, val1, indices1, err );
    MSQ_ERRZERO( err );
    rval2 = metric2.evaluate_with_indices( pd, handle, val2, indices2, err );
    MSQ_ERRZERO( err );

    indices.clear();
    std::sort( indices1.begin(), indices1.end() );
    std::sort( indices2.begin(), indices2.end() );
    std::set_union( indices1.begin(), indices1.end(), indices2.begin(), indices2.end(), std::back_inserter( indices ) );

    value = val1 * val2;
    return rval1 && rval2;
}

bool MultiplyQualityMetric::evaluate_with_gradient( PatchData& pd,
                                                    size_t handle,
                                                    double& value,
                                                    std::vector< size_t >& indices,
                                                    std::vector< Vector3D >& gradient,
                                                    MsqError& err )
{
    std::vector< size_t >::iterator i;
    size_t j;
    double val1, val2;
    bool rval1, rval2;
    rval1 = metric1.evaluate_with_gradient( pd, handle, val1, indices1, grad1, err );
    MSQ_ERRZERO( err );
    rval2 = metric2.evaluate_with_gradient( pd, handle, val2, indices2, grad2, err );
    MSQ_ERRZERO( err );

    indices.resize( indices1.size() + indices2.size() );
    i = std::copy( indices1.begin(), indices1.end(), indices.begin() );
    std::copy( indices2.begin(), indices2.end(), i );
    std::sort( indices.begin(), indices.end() );
    indices.erase( std::unique( indices.begin(), indices.end() ), indices.end() );

    gradient.clear();
    gradient.resize( indices.size(), Vector3D( 0.0 ) );
    for( j = 0; j < indices1.size(); ++j )
    {
        i        = std::lower_bound( indices.begin(), indices.end(), indices1[j] );
        size_t k = i - indices.begin();
        gradient[k] += val2 * grad1[j];
    }
    for( j = 0; j < indices2.size(); ++j )
    {
        i        = std::lower_bound( indices.begin(), indices.end(), indices2[j] );
        size_t k = i - indices.begin();
        gradient[k] += val1 * grad2[j];
    }

    value = val1 * val2;
    return rval1 && rval2;
}

bool MultiplyQualityMetric::evaluate_with_Hessian( PatchData& pd,
                                                   size_t handle,
                                                   double& value,
                                                   std::vector< size_t >& indices,
                                                   std::vector< Vector3D >& gradient,
                                                   std::vector< Matrix3D >& Hessian,
                                                   MsqError& err )
{
    std::vector< size_t >::iterator i;
    size_t j, r, c, n, h;
    double val1, val2;
    bool rval1, rval2;
    rval1 = metric1.evaluate_with_Hessian( pd, handle, val1, indices1, grad1, Hess1, err );
    MSQ_ERRZERO( err );
    rval2 = metric2.evaluate_with_Hessian( pd, handle, val2, indices2, grad2, Hess2, err );
    MSQ_ERRZERO( err );
    // merge index lists
    indices.resize( indices1.size() + indices2.size() );
    i = std::copy( indices1.begin(), indices1.end(), indices.begin() );
    std::copy( indices2.begin(), indices2.end(), i );
    std::sort( indices.begin(), indices.end() );
    indices.erase( std::unique( indices.begin(), indices.end() ), indices.end() );
    // calculate grads and convert index lists to indices into output list
    gradient.clear();
    gradient.resize( indices.size(), Vector3D( 0.0 ) );
    for( j = 0; j < indices1.size(); ++j )
    {
        i           = std::lower_bound( indices.begin(), indices.end(), indices1[j] );
        indices1[j] = i - indices.begin();
        gradient[indices1[j]] += val2 * grad1[j];
    }
    for( j = 0; j < indices2.size(); ++j )
    {
        i           = std::lower_bound( indices.begin(), indices.end(), indices2[j] );
        indices2[j] = i - indices.begin();
        gradient[indices2[j]] += val1 * grad2[j];
    }
    // allocate space for hessians, and zero it
    const size_t N = indices.size();
    Hessian.clear();
    Hessian.resize( N * ( N + 1 ) / 2, Matrix3D( 0.0 ) );
    // add hessian terms from first metric
    n = indices1.size();
    h = 0;
    for( r = 0; r < n; ++r )
    {
        const size_t nr = indices1[r];
        for( c = r; c < n; ++c )
        {
            const size_t nc = indices1[c];
            Hess1[h] *= val2;
            if( nr <= nc )
                Hessian[N * nr - nr * ( nr + 1 ) / 2 + nc] += Hess1[h];
            else
                Hessian[N * nc - nc * ( nc + 1 ) / 2 + nr].plus_transpose_equal( Hess1[h] );
            ++h;
        }
    }
    // add hessian terms from second metric
    n = indices2.size();
    h = 0;
    for( r = 0; r < n; ++r )
    {
        const size_t nr = indices2[r];
        for( c = r; c < n; ++c )
        {
            const size_t nc = indices2[c];
            Hess2[h] *= val1;
            if( nr <= nc )
                Hessian[N * nr - nr * ( nr + 1 ) / 2 + nc] += Hess2[h];
            else
                Hessian[N * nc - nc * ( nc + 1 ) / 2 + nr].plus_transpose_equal( Hess2[h] );
            ++h;
        }
    }
    // add gradient outer products
    n        = indices1.size();
    size_t m = indices2.size();
    Matrix3D outer;
    for( r = 0; r < n; ++r )
    {
        const size_t nr = indices1[r];
        for( c = 0; c < m; ++c )
        {
            const size_t nc = indices2[c];
            outer.outer_product( grad1[r], grad2[c] );
            if( nr == nc )
                Hessian[N * nr - nr * ( nr + 1 ) / 2 + nc] += outer.plus_transpose_equal( outer );
            else if( nr < nc )
                Hessian[N * nr - nr * ( nr + 1 ) / 2 + nc] += outer;
            else
                Hessian[N * nc - nc * ( nc + 1 ) / 2 + nr].plus_transpose_equal( outer );
        }
    }

    value = val1 * val2;
    return rval1 && rval2;
}