gsConjugateGradient.hpp

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#include <gsSolver/gsLanczosMatrix.h>
 
namespace gismo
{
 
template<class T>
bool gsConjugateGradient<T>::initIteration( const typename gsConjugateGradient<T>::VectorType& rhs,
                                            typename gsConjugateGradient<T>::VectorType& x )
{
    if (m_calcEigenvals)
    {
        m_delta.clear();
        m_delta.resize(1,0);
        m_delta.reserve(m_max_iters / 3);
 
        m_gamma.clear();
        m_gamma.reserve(m_max_iters / 3);
    }
 
    if (Base::initIteration(rhs,x))
        return true;
 
    index_t n = m_mat->cols();
    index_t m = 1;                                                      // == rhs.cols();
    m_tmp.resize(n,m);
    m_update.resize(n,m);
 
    m_mat->apply(x,m_tmp);                                              // apply the system matrix
    m_res = rhs - m_tmp;                                                // initial residual
 
    m_error = m_res.norm() / m_rhs_norm;
    if (m_error < m_tol)
        return true;
 
    m_precond->apply(m_res,m_update);                                   // initial search direction
    m_abs_new = m_res.col(0).dot(m_update.col(0));                      // the square of the absolute value of r scaled by invM
 
    return false;
}
 
template<class T>
bool gsConjugateGradient<T>::step( typename gsConjugateGradient<T>::VectorType& x )
{
    m_mat->apply(m_update,m_tmp);                                      // apply system matrix
 
    T alpha = m_abs_new / m_update.col(0).dot(m_tmp.col(0));           // the amount we travel on dir
    if (m_calcEigenvals)
        m_delta.back()+=(1./alpha);
 
    x += alpha * m_update;                                             // update solution
    m_res -= alpha * m_tmp;                                            // update residual
 
    m_error = m_res.norm() / m_rhs_norm;
    if (m_error < m_tol)
        return true;
 
    m_precond->apply(m_res, m_tmp);                                    // approximately solve for "A tmp = residual"
 
    T abs_old = m_abs_new;
 
    m_abs_new = m_res.col(0).dot(m_tmp.col(0));                        // update the absolute value of r
    T beta = m_abs_new / abs_old;                                      // calculate the Gram-Schmidt value used to create the new search direction
    m_update = m_tmp + beta * m_update;                                // update search direction
 
    if (m_calcEigenvals)
    {
        m_gamma.push_back(-math::sqrt(beta)/alpha);
        m_delta.push_back(beta/alpha);
    }
    return false;
}
 
template<class T>
T gsConjugateGradient<T>::getConditionNumber()
{
    if ( m_delta.empty() )
    {
        gsWarn<< "Condition number needs eigenvalues set setCalcEigenvalues(true)"
                 " and call solve with an arbitrary right hand side";
        return -1;
    }
    // If the condition number is calculated before the solver has ended,
    // then we need to scale the last entry
    if (m_error < m_tol)
    {
        gsLanczosMatrix<T> L(m_gamma,m_delta);
        return L.maxEigenvalue()/L.minEigenvalue();
    }
    else
    {
        T tmp_original = m_delta.back();
        m_mat->apply(m_update,m_tmp);
        T alpha = m_abs_new / m_update.col(0).dot(m_tmp.col(0));
        m_delta.back()+=(1./alpha);
        gsLanczosMatrix<T> L(m_gamma,m_delta);
        T result = L.maxEigenvalue()/L.minEigenvalue();
        m_delta.back() = tmp_original;
        return result;
    }
}
 
template<class T>
void gsConjugateGradient<T>::getEigenvalues( typename gsConjugateGradient<T>::VectorType& eigs )
{
    if ( m_delta.empty() )
    {
        gsWarn<< "Eigenvalues were not computed, set setCalcEigenvalues(true)"
                 " and call solve with an arbitrary right hand side";
        eigs.clear();
        return;
    }
 
    gsLanczosMatrix<T> LM(m_gamma,m_delta);
    gsSparseMatrix<T> L = LM.matrix();
    // there is probably a better option...
    typename gsMatrix<T>::SelfAdjEigenSolver eigensolver(L);
    eigs = eigensolver.eigenvalues();
}
 
 
} // end namespace gismo