getfem_model_solvers.h

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/* -*- c++ -*- (enables emacs c++ mode) */
/*===========================================================================
 
 Copyright (C) 2004-2020 Yves Renard
 
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/**
   @file getfem_model_solvers.h
   @author  Yves Renard <Yves.Renard@insa-lyon.fr>
   @date June 15, 2004.
   @brief Standard solvers for model bricks
   @see getfem_modeling.h
*/
 
#ifndef GETFEM_MODEL_SOLVERS_H__
#define GETFEM_MODEL_SOLVERS_H__
#include "getfem_models.h"
#include "gmm/gmm_superlu_interface.h"
#include "gmm/gmm_MUMPS_interface.h"
#include "gmm/gmm_iter.h"
#include "gmm/gmm_iter_solvers.h"
#include "gmm/gmm_dense_qr.h"
 
//#include "gmm/gmm_inoutput.h"
 
 
namespace getfem {
 
  template <typename T> struct sort_abs_val_
  { bool operator()(T x, T y) { return (gmm::abs(x) < gmm::abs(y)); } };
 
  template <typename MAT> void print_eigval(const MAT &M) {
    // just to test a stiffness matrix if needing
    typedef typename gmm::linalg_traits<MAT>::value_type T;
    size_type n = gmm::mat_nrows(M);
    gmm::dense_matrix<T> MM(n, n), Q(n, n);
    std::vector<T> eigval(n);
    gmm::copy(M, MM);
    // gmm::symmetric_qr_algorithm(MM, eigval, Q);
    gmm::implicit_qr_algorithm(MM, eigval, Q);
    std::sort(eigval.begin(), eigval.end(), sort_abs_val_<T>());
    cout << "eival = " << eigval << endl;
//     cout << "vectp : " << gmm::mat_col(Q, n-1) << endl;
//     cout << "vectp : " << gmm::mat_col(Q, n-2) << endl;
//     double emax, emin;
//     cout << "condition number" << condition_number(MM,emax,emin) << endl;
//     cout << "emin = " << emin << endl;
//     cout << "emax = " << emax << endl;
  }
 
 
  /* ***************************************************************** */
  /*     Linear solvers definition                                     */
  /* ***************************************************************** */
 
  template <typename MAT, typename VECT>
  struct abstract_linear_solver {
    typedef MAT MATRIX;
    typedef VECT VECTOR;
    virtual void operator ()(const MAT &, VECT &, const VECT &,
                             gmm::iteration &) const = 0;
    virtual ~abstract_linear_solver() {}
  };
 
  template <typename MAT, typename VECT>
  struct linear_solver_cg_preconditioned_ildlt
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter)  const {
      gmm::ildlt_precond<MAT> P(M);
      gmm::cg(M, x, b, P, iter);
      if (!iter.converged()) GMM_WARNING2("cg did not converge!");
    }
  };
 
  template <typename MAT, typename VECT>
  struct linear_solver_gmres_preconditioned_ilu
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter)  const {
      gmm::ilu_precond<MAT> P(M);
      gmm::gmres(M, x, b, P, 500, iter);
      if (!iter.converged()) GMM_WARNING2("gmres did not converge!");
    }
  };
 
  template <typename MAT, typename VECT>
  struct linear_solver_gmres_unpreconditioned
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter)  const {
      gmm::identity_matrix P;
      gmm::gmres(M, x, b, P, 500, iter);
      if (!iter.converged()) GMM_WARNING2("gmres did not converge!");
    }
  };
 
  template <typename MAT, typename VECT>
  struct linear_solver_gmres_preconditioned_ilut
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter)  const {
      gmm::ilut_precond<MAT> P(M, 40, 1E-7);
      gmm::gmres(M, x, b, P, 500, iter);
      if (!iter.converged()) GMM_WARNING2("gmres did not converge!");
    }
  };
 
  template <typename MAT, typename VECT>
  struct linear_solver_gmres_preconditioned_ilutp
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter)  const {
      gmm::ilutp_precond<MAT> P(M, 20, 1E-7);
      gmm::gmres(M, x, b, P, 500, iter);
      if (!iter.converged()) GMM_WARNING2("gmres did not converge!");
    }
  };
 
#if defined(GMM_USES_SUPERLU)
  template <typename MAT, typename VECT>
  struct linear_solver_superlu
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter)  const {
      double rcond;
      /*gmm::HarwellBoeing_IO::write("test.hb", M);
      std::fstream f("bbb", std::ios::out);
      for (unsigned i=0; i < gmm::vect_size(b); ++i) f << b[i] << "\n";*/
      int info = gmm::SuperLU_solve(M, x, b, rcond);
      iter.enforce_converged(info == 0);
      if (iter.get_noisy()) cout << "condition number: " << 1.0/rcond<< endl;
    }
  };
#endif
 
  template <typename MAT, typename VECT>
  struct linear_solver_dense_lu : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter) const {
      typedef typename gmm::linalg_traits<MAT>::value_type T;
      gmm::dense_matrix<T> MM(gmm::mat_nrows(M),gmm::mat_ncols(M));
      gmm::copy(M, MM);
      gmm::lu_solve(MM, x, b);
      iter.enforce_converged(true);
    }
  };
 
#if defined(GMM_USES_MUMPS)
  template <typename MAT, typename VECT>
  struct linear_solver_mumps : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter) const {
      bool ok = gmm::MUMPS_solve(M, x, b, false);
      iter.enforce_converged(ok);
    }
  };
  template <typename MAT, typename VECT>
  struct linear_solver_mumps_sym : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter) const {
      bool ok = gmm::MUMPS_solve(M, x, b, true);
      iter.enforce_converged(ok);
    }
  };
#endif
 
#if GETFEM_PARA_LEVEL > 1 && GETFEM_PARA_SOLVER == MUMPS_PARA_SOLVER
  template <typename MAT, typename VECT>
  struct linear_solver_distributed_mumps
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter) const {
      double tt_ref=MPI_Wtime();
      bool ok = MUMPS_distributed_matrix_solve(M, x, b, false);
      iter.enforce_converged(ok);
      if (MPI_IS_MASTER()) cout<<"UNSYMMETRIC MUMPS time "<< MPI_Wtime() - tt_ref<<endl;
    }
  };
 
  template <typename MAT, typename VECT>
  struct linear_solver_distributed_mumps_sym
    : public abstract_linear_solver<MAT, VECT> {
    void operator ()(const MAT &M, VECT &x, const VECT &b,
                     gmm::iteration &iter) const {
      double tt_ref=MPI_Wtime();
      bool ok = MUMPS_distributed_matrix_solve(M, x, b, true);
      iter.enforce_converged(ok);
      if (MPI_IS_MASTER()) cout<<"SYMMETRIC MUMPS time "<< MPI_Wtime() - tt_ref<<endl;
    }
  };
#endif
 
 
  /* ***************************************************************** */
  /*     Newton Line search definition                                 */
  /* ***************************************************************** */
 
  struct abstract_newton_line_search {
    double conv_alpha, conv_r;
    size_t it, itmax, glob_it;
    //  size_t tot_it;
    virtual void init_search(double r, size_t git, double R0 = 0.0) = 0;
    virtual double next_try(void) = 0;
    virtual bool is_converged(double, double R1 = 0.0) = 0;
    virtual double converged_value(void) {
      // tot_it += it; cout << "tot_it = " << tot_it << endl; it = 0;
      return conv_alpha;
    };
    virtual double converged_residual(void) { return conv_r; };
    virtual ~abstract_newton_line_search() { }
  };
 
  // Dummy linesearch for the newton with step control
  struct newton_search_with_step_control : public abstract_newton_line_search {
 
    virtual void init_search(double /*r*/, size_t /*git*/, double /*R0*/ = 0.0)
    { GMM_ASSERT1(false, "Not to be used"); }
 
    virtual double next_try(void)
    { GMM_ASSERT1(false, "Not to be used"); }
 
    virtual bool is_converged(double /*r*/, double /*R1*/ = 0.0)
    { GMM_ASSERT1(false, "Not to be used"); }
 
    newton_search_with_step_control() {}
  };
 
 
  struct quadratic_newton_line_search : public abstract_newton_line_search {
    double R0_, R1_;
 
    double alpha, first_res;
    virtual void init_search(double r, size_t git, double R0 = 0.0) {
      GMM_ASSERT1(R0 != 0.0, "You have to specify R0");
      glob_it = git;
      conv_alpha = alpha = double(1); conv_r = first_res = r; it = 0;
      R0_ = R0;
    }
    virtual double next_try(void) {
      ++it;
      if (it == 1) return double(1);
      GMM_ASSERT1(R1_ != 0.0, "You have to specify R1");
      double a = R0_ / R1_;
      return (a < 0) ? (a*0.5 + sqrt(a*a*0.25-a)) : a*0.5;
    }
    virtual bool is_converged(double r, double R1 = 0.0) {
      conv_r = r;
      R1_ = R1; return ((gmm::abs(R1_) < gmm::abs(R0_*0.5)) || it >= itmax);
    }
    quadratic_newton_line_search(size_t imax = size_t(-1))  { itmax = imax; }
  };
 
 
  struct simplest_newton_line_search : public abstract_newton_line_search {
    double alpha, alpha_mult, first_res, alpha_max_ratio, alpha_min,
           alpha_threshold_res;
    virtual void init_search(double r, size_t git, double = 0.0) {
      glob_it = git;
      conv_alpha = alpha = double(1); conv_r = first_res = r; it = 0;
    }
    virtual double next_try(void)
    { conv_alpha = alpha; alpha *= alpha_mult; ++it; return conv_alpha; }
    virtual bool is_converged(double r, double = 0.0) {
      conv_r = r;
      return ((it <= 1 && r < first_res)
              || (r <= first_res * alpha_max_ratio && r <= alpha_threshold_res)
              || (conv_alpha <= alpha_min && r < first_res * 1e5)
              || it >= itmax);
    }
    simplest_newton_line_search
    (size_t imax = size_t(-1), double a_max_ratio = 6.0/5.0,
     double a_min = 1.0/1000.0, double a_mult = 3.0/5.0,
     double a_threshold_res = 1e50)
      : alpha_mult(a_mult), alpha_max_ratio(a_max_ratio), alpha_min(a_min),
        alpha_threshold_res(a_threshold_res)
      { itmax = imax; }
  };
 
  struct default_newton_line_search : public abstract_newton_line_search {
    // This line search try to detect where is the minimum, dividing the step
    // by a factor two each time.
    //    - it stops if the residual is less than the previous residual
    //      times alpha_min_ratio (= 0.9).
    //    - if the minimal step is reached with a residual greater than
    //      the previous residual times alpha_min_ratio then it decides
    //      between two options :
    //        - return with the step corresponding to the smallest residual
    //        - return with a greater residual corresponding to a residual
    //          less than the previous residual times alpha_max_ratio.
    //      the decision is taken regarding the previous iterations.
    //    - in order to shorten the line search, the process stops when
    //      the residual increases three times consecutively.
    // possible improvment : detect the curvature at the origin
    // (only one evaluation) and take it into account.
    // Fitted to some experiments in contrib/tests_newton
 
    double alpha, alpha_old, alpha_mult, first_res, alpha_max_ratio;
    double alpha_min_ratio, alpha_min;
    size_type count, count_pat;
    bool max_ratio_reached;
    double alpha_max_ratio_reached, r_max_ratio_reached;
    size_type it_max_ratio_reached;
 
    virtual void init_search(double r, size_t git, double = 0.0);
    virtual double next_try(void);
    virtual bool is_converged(double r, double = 0.0);
    default_newton_line_search(void) { count_pat = 0; }
  };
 
  /* the former default_newton_line_search */
  struct basic_newton_line_search : public abstract_newton_line_search {
    double alpha, alpha_mult, first_res, alpha_max_ratio;
    double alpha_min, prev_res, alpha_max_augment;
    virtual void init_search(double r, size_t git, double = 0.0) {
      glob_it = git;
      conv_alpha = alpha = double(1);
      prev_res = conv_r = first_res = r; it = 0;
    }
    virtual double next_try(void)
    { conv_alpha = alpha; alpha *= alpha_mult; ++it; return conv_alpha; }
    virtual bool is_converged(double r, double = 0.0) {
      if (glob_it == 0 || (r < first_res / double(2))
          || (conv_alpha <= alpha_min && r < first_res * alpha_max_augment)
          || it >= itmax)
        { conv_r = r; return true; }
      if (it > 1 && r > prev_res && prev_res < alpha_max_ratio * first_res)
        return true;
      prev_res = conv_r = r;
      return false;
    }
    basic_newton_line_search
    (size_t imax = size_t(-1),
     double a_max_ratio = 5.0/3.0,
     double a_min = 1.0/1000.0, double a_mult = 3.0/5.0, double a_augm = 2.0)
      : alpha_mult(a_mult), alpha_max_ratio(a_max_ratio),
        alpha_min(a_min), alpha_max_augment(a_augm) { itmax = imax; }
  };
 
 
  struct systematic_newton_line_search : public abstract_newton_line_search {
    double alpha, alpha_mult, first_res;
    double alpha_min, prev_res;
    bool first;
    virtual void init_search(double r, size_t git, double = 0.0) {
      glob_it = git;
      conv_alpha = alpha = double(1);
      prev_res = conv_r = first_res = r; it = 0; first = true;
    }
    virtual double next_try(void)
    { double a = alpha; alpha *= alpha_mult; ++it; return a; }
    virtual bool is_converged(double r, double = 0.0) {
      // cout << "a = " << alpha / alpha_mult << " r = " << r << endl;
      if (r < conv_r || first)
        { conv_r = r; conv_alpha = alpha / alpha_mult; first = false; }
      if ((alpha <= alpha_min*alpha_mult) || it >= itmax) return true;
      return false;
    }
    systematic_newton_line_search
    (size_t imax = size_t(-1),
     double a_min = 1.0/10000.0, double a_mult = 3.0/5.0)
      : alpha_mult(a_mult), alpha_min(a_min)  { itmax = imax; }
  };
 
 
  /* ***************************************************************** */
  /*     Newton(-Raphson) algorithm with step control.                 */
  /* ***************************************************************** */
  /* Still solves a problem F(X) = 0 starting at X0 but setting        */
  /* B0 = F(X0) and solving                                            */
  /* F(X) = (1-alpha)B0       (1)                                      */
  /* with alpha growing from 0 to 1.                                   */
  /* A very basic line search is applied.                              */
  /*                                                                   */
  /* Step 0 : set alpha0 = 0, alpha = 1, X0 given and B0 = F(X0).      */
  /* Step 1 : Set Ri = F(Xi) - (1-alpha)B0                             */
  /*          If ||Ri|| < rho, Xi+1 = Xi and go to step 2              */
  /*          Perform Newton step on problem (1)                       */
  /*          If the Newton do not converge (stagnation)               */
  /*            alpha <- max(alpha0+1E-4, (alpha+alpha0)/2)            */
  /*            Loop on step 1 with Xi unchanged                       */
  /* Step 2 : if alpha=1 stop                                          */
  /*          else alpha0 <- alpha,                                    */
  /*              alpha <- min(1,alpha0+2*(alpha-alpha0)),             */
  /*              Go to step 1 with Xi+1                               */
  /*              being the result of Newton iterations of step1.      */
  /*                                                                   */
  /*********************************************************************/
 
  template <typename PB>
  void Newton_with_step_control(PB &pb, gmm::iteration &iter)
  {
    typedef typename gmm::linalg_traits<typename PB::VECTOR>::value_type T;
    typedef typename gmm::number_traits<T>::magnitude_type R;
    gmm::iteration iter_linsolv0 = iter;
    iter_linsolv0.reduce_noisy();
    iter_linsolv0.set_resmax(iter.get_resmax()/20.0);
    iter_linsolv0.set_maxiter(10000); // arbitrary
 
    pb.compute_residual();
    R approx_eln = pb.approx_external_load_norm();
    if (approx_eln == R(0)) approx_eln = R(1);
 
    typename PB::VECTOR b0(gmm::vect_size(pb.residual()));
    gmm::copy(pb.residual(), b0);
    typename PB::VECTOR Xi(gmm::vect_size(pb.residual()));
    gmm::copy(pb.state_vector(), Xi);
 
    typename PB::VECTOR dr(gmm::vect_size(pb.residual()));
 
    R alpha0(0), alpha(1), res0(gmm::vect_norm1(b0)), minres(res0);
    // const newton_search_with_step_control *ls
    //  = dynamic_cast<const newton_search_with_step_control *>(&(pb.ls));
    // GMM_ASSERT1(ls, "Internal error");
    size_type nit = 0, stagn = 0;
    R coeff = R(2);
 
    scalar_type crit = pb.residual_norm() / approx_eln;
    if (iter.finished(crit)) return;
    for(;;) {
 
      crit = gmm::vect_dist1(pb.residual(), gmm::scaled(b0, R(1)-alpha))
        / approx_eln;
      if (!iter.converged(crit)) {
        gmm::iteration iter_linsolv = iter_linsolv0;
 
        int is_singular = 1;
        while (is_singular) { // Linear system solve
          gmm::clear(dr);
          pb.add_to_residual(b0, alpha-R(1)); // canceled at next compute_residual
          iter_linsolv.init();
          if (iter.get_noisy() > 1)
            cout << "starting tangent matrix computation" << endl;
          pb.compute_tangent_matrix();
          if (iter.get_noisy() > 1)
            cout << "starting linear solver" << endl;
          pb.linear_solve(dr, iter_linsolv);
          if (!iter_linsolv.converged()) {
            is_singular++;
            if (is_singular <= 4) {
              if (iter.get_noisy())
                cout << "Singular tangent matrix:"
                  " perturbation of the state vector." << endl;
              pb.perturbation();
              pb.compute_residual(); // cancels add_to_residual
            } else {
              if (iter.get_noisy())
                cout << "Singular tangent matrix: perturbation failed, "
                     << "aborting." << endl;
              return;
            }
          }
          else is_singular = 0;
        }
        if (iter.get_noisy() > 1) cout << "linear solver done" << endl;
 
        gmm::add(dr, pb.state_vector());
        pb.compute_residual(); // cancels add_to_residual
        R res = gmm::vect_dist1(pb.residual(), gmm::scaled(b0, R(1)-alpha));
        R dec = R(1)/R(2), coeff2 = coeff * R(1.5);
 
        while (dec > R(1E-5) && res >= res0 * coeff) {
          gmm::add(gmm::scaled(dr, -dec), pb.state_vector());
          pb.compute_residual();
          R res2 = gmm::vect_dist1(pb.residual(), gmm::scaled(b0, R(1)-alpha));
          if (res2 < res*R(0.95) || res2 >= res0 * coeff2) {
            dec /= R(2); res = res2; coeff2 *= R(1.5);
          } else {
            gmm::add(gmm::scaled(dr, dec), pb.state_vector());
            break;
          }
        }
        dec *= R(2);
 
        nit++;
        coeff = std::max(R(1.05), coeff*R(0.93));
        bool near_end = (iter.get_iteration() > iter.get_maxiter()/2);
        bool cut = (alpha < R(1)) && near_end;
        if ((res > minres && nit > 4) || cut) {
          stagn++;
 
          if ((stagn > 10 && alpha > alpha0 + R(5E-2)) || cut) {
            alpha = (alpha + alpha0) / R(2);
            if (near_end) alpha = R(1);
            gmm::copy(Xi, pb.state_vector());
            pb.compute_residual();
            res = gmm::vect_dist1(pb.residual(), gmm::scaled(b0, R(1)-alpha));
            nit = 0;
            stagn = 0; coeff = R(2);
          }
        }
        if (res < minres || (alpha == R(1) &&  nit < 10)) stagn = 0;
        res0 = res;
        if (nit < 5) minres = res0; else minres = std::min(minres, res0);
 
        if (iter.get_noisy())
          cout << "step control [" << std::setw(8) << alpha0 << ","
               << std::setw(8) << alpha << "," << std::setw(10) << dec <<  "]";
        ++iter;
        // crit = std::min(res / approx_eln,
        //                gmm::vect_norm1(dr) / std::max(1E-25,pb.state_norm()));
        crit = res / approx_eln;
      }
 
      if (iter.finished(crit)) {
        if (iter.converged() && alpha < R(1)) {
          R a = alpha;
          alpha = std::min(R(1), alpha*R(3) - alpha0*R(2));
          alpha0 = a;
          gmm::copy(pb.state_vector(), Xi);
          nit = 0; stagn = 0; coeff = R(2);
        } else return;
      }
    }
  }
 
 
 
  /* ***************************************************************** */
  /*     Classical Newton(-Raphson) algorithm.                         */
  /* ***************************************************************** */
 
  template <typename PB>
  void classical_Newton(PB &pb, gmm::iteration &iter)
  {
    typedef typename gmm::linalg_traits<typename PB::VECTOR>::value_type T;
    typedef typename gmm::number_traits<T>::magnitude_type R;
    gmm::iteration iter_linsolv0 = iter;
    iter_linsolv0.reduce_noisy();
    iter_linsolv0.set_resmax(iter.get_resmax()/20.0);
    iter_linsolv0.set_maxiter(10000); // arbitrary
 
    pb.compute_residual();
    R approx_eln = pb.approx_external_load_norm();
    if (approx_eln == R(0)) approx_eln = R(1);
 
    typename PB::VECTOR dr(gmm::vect_size(pb.residual()));
 
    scalar_type crit = pb.residual_norm() / approx_eln;
    while (!iter.finished(crit)) {
      gmm::iteration iter_linsolv = iter_linsolv0;
 
      int is_singular = 1;
      while (is_singular) {
        gmm::clear(dr);
        iter_linsolv.init();
        if (iter.get_noisy() > 1)
          cout << "starting computing tangent matrix" << endl;
        pb.compute_tangent_matrix();
        if (iter.get_noisy() > 1)
          cout << "starting linear solver" << endl;
        pb.linear_solve(dr, iter_linsolv);
        if (!iter_linsolv.converged()) {
          is_singular++;
          if (is_singular <= 4) {
            if (iter.get_noisy())
              cout << "Singular tangent matrix:"
                " perturbation of the state vector." << endl;
            pb.perturbation();
            pb.compute_residual();
          } else {
            if (iter.get_noisy())
              cout << "Singular tangent matrix: perturbation failed, aborting."
                   << endl;
            return;
          }
        }
        else is_singular = 0;
      }
 
      if (iter.get_noisy() > 1) cout << "linear solver done" << endl;
      R alpha = pb.line_search(dr, iter); //it is assumed that the linesearch
                                          //executes a pb.compute_residual();
      if (iter.get_noisy()) cout << "alpha = " << std::setw(6) << alpha << " ";
      ++iter;
      crit = std::min(pb.residual_norm() / approx_eln,
                      gmm::vect_norm1(dr) / std::max(1E-25, pb.state_norm()));
    }
  }
 
 
 
  //---------------------------------------------------------------------
  // Default linear solver.
  //---------------------------------------------------------------------
 
  typedef std::shared_ptr<abstract_linear_solver<model_real_sparse_matrix,
                                                 model_real_plain_vector> >
    rmodel_plsolver_type;
  typedef std::shared_ptr<abstract_linear_solver<model_complex_sparse_matrix,
                                                 model_complex_plain_vector> >
    cmodel_plsolver_type;
 
  template<typename MATRIX, typename VECTOR>
  std::shared_ptr<abstract_linear_solver<MATRIX, VECTOR>>
  default_linear_solver(const model &md) {
 
#if GETFEM_PARA_LEVEL == 1 && GETFEM_PARA_SOLVER == MUMPS_PARA_SOLVER
    if (md.is_symmetric())
      return std::make_shared<linear_solver_mumps_sym<MATRIX, VECTOR>>();
    else
      return std::make_shared<linear_solver_mumps<MATRIX, VECTOR>>();
#elif GETFEM_PARA_LEVEL > 1 && GETFEM_PARA_SOLVER == MUMPS_PARA_SOLVER
    if (md.is_symmetric())
      return std::make_shared
        <linear_solver_distributed_mumps_sym<MATRIX, VECTOR>>();
      else
        return std::make_shared
          <linear_solver_distributed_mumps<MATRIX, VECTOR>>();
#else
    size_type ndof = md.nb_dof(), max3d = 15000, dim = md.leading_dimension();
# if defined(GMM_USES_MUMPS)
    max3d = 250000;
# endif
    if ((ndof<300000 && dim<=2) || (ndof<max3d && dim<=3) || (ndof<1000)) {
# if defined(GMM_USES_MUMPS)
      if (md.is_symmetric())
        return std::make_shared<linear_solver_mumps_sym<MATRIX, VECTOR>>();
      else
        return std::make_shared<linear_solver_mumps<MATRIX, VECTOR>>();
# elif defined(GMM_USES_SUPERLU)
      return std::make_shared<linear_solver_superlu<MATRIX, VECTOR>>();
# else
      static_assert(false,
                    "At least one direct solver (MUMPS or SuperLU) is required");
# endif
    }
    else {
      if (md.is_coercive())
        return std::make_shared
          <linear_solver_cg_preconditioned_ildlt<MATRIX, VECTOR>>();
      else {
        if (dim <= 2)
          return std::make_shared
            <linear_solver_gmres_preconditioned_ilut<MATRIX,VECTOR>>();
          else
            return std::make_shared
              <linear_solver_gmres_preconditioned_ilu<MATRIX,VECTOR>>();
      }
    }
#endif
    return std::shared_ptr<abstract_linear_solver<MATRIX, VECTOR>>();
  }
 
  template <typename MATRIX, typename VECTOR>
  std::shared_ptr<abstract_linear_solver<MATRIX, VECTOR>>
  select_linear_solver(const model &md, const std::string &name) {
    std::shared_ptr<abstract_linear_solver<MATRIX, VECTOR>> p;
    if (bgeot::casecmp(name, "superlu") == 0) {
#if defined(GMM_USES_SUPERLU)
      return std::make_shared<linear_solver_superlu<MATRIX, VECTOR>>();
#else
      GMM_ASSERT1(false, "SuperLU is not interfaced");
#endif
    }
    else if (bgeot::casecmp(name, "dense_lu") == 0)
      return std::make_shared<linear_solver_dense_lu<MATRIX, VECTOR>>();
    else if (bgeot::casecmp(name, "mumps") == 0) {
#if defined(GMM_USES_MUMPS)
# if GETFEM_PARA_LEVEL <= 1
      return std::make_shared<linear_solver_mumps<MATRIX, VECTOR>>();
# else
      return std::make_shared
        <linear_solver_distributed_mumps<MATRIX, VECTOR>>();
# endif
#else
      GMM_ASSERT1(false, "Mumps is not interfaced");
#endif
    }
    else if (bgeot::casecmp(name, "cg/ildlt") == 0)
      return std::make_shared
        <linear_solver_cg_preconditioned_ildlt<MATRIX, VECTOR>>();
    else if (bgeot::casecmp(name, "gmres/ilu") == 0)
      return std::make_shared
        <linear_solver_gmres_preconditioned_ilu<MATRIX, VECTOR>>();
    else if (bgeot::casecmp(name, "gmres/ilut") == 0)
      return std::make_shared
        <linear_solver_gmres_preconditioned_ilut<MATRIX, VECTOR>>();
    else if (bgeot::casecmp(name, "gmres/ilutp") == 0)
      return std::make_shared
        <linear_solver_gmres_preconditioned_ilutp<MATRIX, VECTOR>>();
    else if (bgeot::casecmp(name, "auto") == 0)
      return default_linear_solver<MATRIX, VECTOR>(md);
    else
      GMM_ASSERT1(false, "Unknown linear solver");
    return std::shared_ptr<abstract_linear_solver<MATRIX, VECTOR>>();
  }
 
  inline rmodel_plsolver_type rselect_linear_solver(const model &md,
                                             const std::string &name) {
    return select_linear_solver<model_real_sparse_matrix,
                                model_real_plain_vector>(md, name);
  }
 
  inline cmodel_plsolver_type cselect_linear_solver(const model &md,
                                             const std::string &name) {
    return select_linear_solver<model_complex_sparse_matrix,
                                model_complex_plain_vector>(md, name);
  }
 
  //---------------------------------------------------------------------
  // Standard solve.
  //---------------------------------------------------------------------
 
 
  /** A default solver for the model brick system.
  Of course it could be not very well suited for a particular
  problem, so it could be copied and adapted to change solvers,
  add a special traitement on the problem, etc ...  This is in
  fact a model for your own solver.
 
  For small problems, a direct solver is used (gmm::SuperLU_solve),
  for larger problems, a conjugate gradient gmm::cg (if the problem is
  coercive) or a gmm::gmres is used (preconditioned with an incomplete
  factorization).
 
  When MPI/METIS is enabled, a partition is done via METIS, and a parallel
  solver can be used.
 
  Note that it is possible to disable some variables
  (see the md.disable_variable(varname) method) in order to
  solve the problem only with respect to a subset of variables (the
  disabled variables are the considered as data) for instance to
  replace the global Newton strategy with a fixed point one.
 
  @ingroup bricks
  */
  void standard_solve(model &md, gmm::iteration &iter,
                      rmodel_plsolver_type lsolver,
                      abstract_newton_line_search &ls);
 
  void standard_solve(model &md, gmm::iteration &iter,
                      cmodel_plsolver_type lsolver,
                      abstract_newton_line_search &ls);
 
  void standard_solve(model &md, gmm::iteration &iter,
                      rmodel_plsolver_type lsolver);
 
  void standard_solve(model &md, gmm::iteration &iter,
                      cmodel_plsolver_type lsolver);
 
  void standard_solve(model &md, gmm::iteration &iter);
 
}  /* end of namespace getfem.                                             */
 
 
#endif /* GETFEM_MODEL_SOLVERS_H__  */