/usr/include/dolfin/la/uBLASKrylovSolver.h is in libdolfin1.3-dev 1.3.0+dfsg-2.
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//
// This file is part of DOLFIN.
//
// DOLFIN 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 3 of the License, or
// (at your option) any later version.
//
// DOLFIN 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
// along with DOLFIN. If not, see <http://www.gnu.org/licenses/>.
//
// Modified by Anders Logg 2006-2012
//
// First added: 2006-05-31
// Last changed: 2012-08-20
#ifndef __UBLAS_KRYLOV_SOLVER_H
#define __UBLAS_KRYLOV_SOLVER_H
#include <set>
#include <string>
#include <boost/shared_ptr.hpp>
#include <dolfin/common/types.h>
#include "ublas.h"
#include "GenericLinearSolver.h"
#include "uBLASLinearOperator.h"
#include "uBLASMatrix.h"
#include "uBLASVector.h"
#include "uBLASPreconditioner.h"
namespace dolfin
{
class GenericLinearOperator;
class GenericVector;
/// This class implements Krylov methods for linear systems
/// of the form Ax = b using uBLAS data types.
class uBLASKrylovSolver : public GenericLinearSolver
{
public:
/// Create Krylov solver for a particular method and preconditioner
uBLASKrylovSolver(std::string method="default",
std::string preconditioner="default");
/// Create Krylov solver for a particular uBLASPreconditioner
uBLASKrylovSolver(uBLASPreconditioner& pc);
/// Create Krylov solver for a particular method and uBLASPreconditioner
uBLASKrylovSolver(std::string method,
uBLASPreconditioner& pc);
/// Destructor
~uBLASKrylovSolver();
/// Solve the operator (matrix)
void set_operator(const boost::shared_ptr<const GenericLinearOperator> A)
{ set_operators(A, A); }
/// Set operator (matrix) and preconditioner matrix
void set_operators(const boost::shared_ptr<const GenericLinearOperator> A,
const boost::shared_ptr<const GenericLinearOperator> P)
{ _A = A; _P = P; }
/// Return the operator (matrix)
const GenericLinearOperator& get_operator() const
{
if (!_A)
{
dolfin_error("uBLASKrylovSolver.cpp",
"access operator for uBLAS Krylov solver",
"Operator has not been set");
}
return *_A;
}
/// Solve linear system Ax = b and return number of iterations
std::size_t solve(GenericVector& x, const GenericVector& b);
/// Solve linear system Ax = b and return number of iterations
std::size_t solve(const GenericLinearOperator& A, GenericVector& x,
const GenericVector& b);
/// Return a list of available solver methods
static std::vector<std::pair<std::string, std::string> > methods();
/// Return a list of available preconditioners
static std::vector<std::pair<std::string, std::string> > preconditioners();
/// Default parameter values
static Parameters default_parameters();
private:
/// Select solver and solve linear system Ax = b and return number of iterations
template<typename MatA, typename MatP>
std::size_t solve_krylov(const MatA& A,
uBLASVector& x,
const uBLASVector& b,
const MatP& P);
/// Solve linear system Ax = b using CG
template<typename Mat>
std::size_t solveCG(const Mat& A, uBLASVector& x, const uBLASVector& b,
bool& converged) const;
/// Solve linear system Ax = b using restarted GMRES
template<typename Mat>
std::size_t solveGMRES(const Mat& A, uBLASVector& x, const uBLASVector& b,
bool& converged) const;
/// Solve linear system Ax = b using BiCGStab
template<typename Mat>
std::size_t solveBiCGStab(const Mat& A, uBLASVector& x, const uBLASVector& b,
bool& converged) const;
/// Select and create named preconditioner
void select_preconditioner(std::string preconditioner);
/// Read solver parameters
void read_parameters();
/// Krylov method
std::string _method;
/// Preconditioner
boost::shared_ptr<uBLASPreconditioner> _pc;
/// Solver parameters
double rtol, atol, div_tol;
std::size_t max_it, restart;
bool report;
/// Operator (the matrix)
boost::shared_ptr<const GenericLinearOperator> _A;
/// Matrix used to construct the preconditoner
boost::shared_ptr<const GenericLinearOperator> _P;
};
//---------------------------------------------------------------------------
// Implementation of template functions
//---------------------------------------------------------------------------
template<typename MatA, typename MatP>
std::size_t uBLASKrylovSolver::solve_krylov(const MatA& A,
uBLASVector& x,
const uBLASVector& b,
const MatP& P)
{
// Check dimensions
std::size_t M = A.size(0);
std::size_t N = A.size(1);
if ( N != b.size() )
{
dolfin_error("uBLASKrylovSolver.h",
"solve linear system using uBLAS Krylov solver",
"Non-matching dimensions for linear system");
}
// Reinitialise x if necessary
if (x.size() != b.size())
{
x.resize(b.size());
x.zero();
}
// Read parameters if not done
read_parameters();
// Write a message
if (report)
info("Solving linear system of size %d x %d (uBLAS Krylov solver).", M, N);
// Initialise preconditioner if necessary
_pc->init(P);
// Choose solver and solve
bool converged = false;
std::size_t iterations = 0;
if (_method == "cg")
iterations = solveCG(A, x, b, converged);
else if (_method == "gmres")
iterations = solveGMRES(A, x, b, converged);
else if (_method == "bicgstab")
iterations = solveBiCGStab(A, x, b, converged);
else if (_method == "default")
iterations = solveBiCGStab(A, x, b, converged);
else
{
dolfin_error("uBLASKrylovSolver.h",
"solve linear system using uBLAS Krylov solver",
"Requested Krylov method (\"%s\") is unknown", _method.c_str());
}
// Check for convergence
if (!converged)
{
bool error_on_nonconvergence = parameters["error_on_nonconvergence"];
if (error_on_nonconvergence)
{
dolfin_error("uBLASKrylovSolver.h",
"solve linear system using uBLAS Krylov solver",
"Solution failed to converge");
}
else
warning("uBLAS Krylov solver failed to converge.");
}
else if (report)
info("Krylov solver converged in %d iterations.", iterations);
return iterations;
}
//-----------------------------------------------------------------------------
template<typename Mat>
std::size_t uBLASKrylovSolver::solveCG(const Mat& A,
uBLASVector& x,
const uBLASVector& b,
bool& converged) const
{
warning("Conjugate-gradient method not yet programmed for uBLASKrylovSolver. Using GMRES.");
return solveGMRES(A, x, b, converged);
}
//-----------------------------------------------------------------------------
template<typename Mat>
std::size_t uBLASKrylovSolver::solveGMRES(const Mat& A, uBLASVector& x,
const uBLASVector& b,
bool& converged) const
{
// Get underlying uBLAS vectors
ublas_vector& _x = x.vec();
const ublas_vector& _b = b.vec();
// Get size of system
const std::size_t size = A.size(0);
// Create residual vector
uBLASVector r(size);
ublas_vector& _r = r.vec();
// Create H matrix and h vector
ublas_matrix_cmajor_tri H(restart, restart);
ublas_vector _h(restart+1);
// Create gamma vector
ublas_vector _gamma(restart+1);
// Matrix containing v_k as columns.
ublas_matrix_cmajor V(size, restart+1);
// w vector
uBLASVector w(size);
ublas_vector& _w = w.vec();
// Givens vectors
ublas_vector _c(restart), _s(restart);
// Miscellaneous storage
double nu, temp1, temp2, r_norm = 0.0, beta0 = 0;
converged = false;
std::size_t iteration = 0;
while (iteration < max_it && !converged)
{
// Compute residual r = b -A*x
//noalias(r) = b;
//axpy_prod(A, -x, r, false);
A.mult(x, r);
_r *= -1.0;
noalias(_r) += _b;
// Apply preconditioner (use w for temporary storage)
_w.assign(_r);
_pc->solve(r, w);
// L2 norm of residual (for most recent restart)
const double beta = norm_2(_r);
// Save intial residual (from restart 0)
if(iteration == 0)
beta0 = beta;
if(beta < atol)
{
converged = true;
return iteration;
}
// Intialise gamma
_gamma.clear();
_gamma(0) = beta;
// Create first column of V
noalias(column(V, 0)) = _r/beta;
// Modified Gram-Schmidt procedure
std::size_t subiteration = 0;
std::size_t j = 0;
while (subiteration < restart && iteration < max_it && !converged && r_norm/beta < div_tol)
{
// Compute product w = A*V_j (use r for temporary storage)
//axpy_prod(A, column(V, j), w, true);
noalias(_r) = column(V, j);
A.mult(r, w);
// Apply preconditioner (use r for temporary storage)
_r.assign(_w);
_pc->solve(w, r);
for (std::size_t i=0; i <= j; ++i)
{
_h(i)= inner_prod(_w, column(V,i));
noalias(_w) -= _h(i)*column(V,i);
}
_h(j+1) = norm_2(_w);
// Insert column of V (inserting v_(j+1)
noalias(column(V,j+1)) = _w/_h(j+1);
// Apply previous Givens rotations to the "new" column
// (this could be improved? - use more uBLAS functions.
// The below has been taken from old DOLFIN code.)
for(std::size_t i=0; i<j; ++i)
{
temp1 = _h(i);
temp2 = _h(i+1);
_h(i) = _c(i)*temp1 - _s(i)*temp2;
_h(i+1) = _s(i)*temp1 + _c(i)*temp2 ;
}
// Compute new c_i and s_i
nu = sqrt( _h(j)*_h(j) + _h(j+1)*_h(j+1) );
// Direct access to c & s below leads to some strange compiler errors
// when using vector expressions and noalias(). By using "subrange",
// we are working with vector expressions rather than reals
//c(j) = h(j)/nu;
//s(j) = -h(j+1)/nu;
subrange(_c, j,j+1) = subrange(_h, j,j+1)/nu;
subrange(_s, j,j+1) = -subrange(_h, j+1,j+2)/nu;
// Apply new rotation to last column
_h(j) = _c(j)*_h(j) - _s(j)*_h(j+1);
_h(j+1) = 0.0;
// Apply rotations to gamma
temp1 = _c(j)*_gamma(j) - _s(j)*_gamma(j+1);
_gamma(j+1) = _s(j)*_gamma(j) + _c(j)*_gamma(j+1);
_gamma(j) = temp1;
r_norm = fabs(_gamma(j+1));
// Add h to H matrix. Would ne nice to use
// noalias(column(H, j)) = subrange(h, 0, restart);
// but this gives an error when uBLAS debugging is turned onand H
// is a triangular matrix
for(std::size_t i=0; i<j+1; ++i)
H(i,j) = _h(i);
// Check for convergence
if( r_norm/beta0 < rtol || r_norm < atol )
converged = true;
++iteration;
++subiteration;
++j;
}
// Eliminate extra rows and columns (this does not resize or copy, just addresses a range)
ublas_matrix_cmajor_tri_range Htrunc(H, ublas::range(0,subiteration), ublas::range(0,subiteration) );
ublas_vector_range _g(_gamma, ublas::range(0,subiteration));
// Solve triangular system H*g and return result in g
ublas::inplace_solve(Htrunc, _g, ublas::upper_tag ());
// x_m = x_0 + V*y
ublas_matrix_cmajor_range _v( V, ublas::range(0,V.size1()), ublas::range(0,subiteration) );
axpy_prod(_v, _g, _x, false);
}
return iteration;
}
//-----------------------------------------------------------------------------
template<typename Mat>
std::size_t uBLASKrylovSolver::solveBiCGStab(const Mat& A,
uBLASVector& x,
const uBLASVector& b,
bool& converged) const
{
// Get uderlying uBLAS vectors
ublas_vector& _x = x.vec();
const ublas_vector& _b = b.vec();
// Get size of system
const std::size_t size = A.size(0);
// Allocate vectors
uBLASVector r(size), rstar(size), p(size), s(size), v(size), t(size), y(size), z(size);
ublas_vector& _r = r.vec();
ublas_vector& _rstar = rstar.vec();
ublas_vector& _p = p.vec();
ublas_vector& _s = s.vec();
ublas_vector& _v = v.vec();
ublas_vector& _t = t.vec();
ublas_vector& _y = y.vec();
ublas_vector& _z = z.vec();
double alpha = 1.0, beta = 0.0, omega = 1.0, r_norm = 0.0;
double rho_old = 1.0, rho = 1.0;
// Compute residual r = b -A*x
//r.assign(b);
//axpy_prod(A, -x, r, false);
A.mult(x, r);
r *= -1.0;
noalias(_r) += _b;
const double r0_norm = norm_2(_r);
if( r0_norm < atol )
{
converged = true;
return 0;
}
// Initialise r^star, v and p
_rstar.assign(_r);
_v.clear();
_p.clear();
// Apply preconditioner to r^start. This is a trick to avoid problems in which
// (r^start, r) = 0 after the first iteration (such as PDE's with homogeneous
// Neumann bc's and no forcing/source term.
_pc->solve(rstar, r);
// Right-preconditioned Bi-CGSTAB
// Start iterations
converged = false;
std::size_t iteration = 0;
while (iteration < max_it && !converged && r_norm/r0_norm < div_tol)
{
// Set rho_n = rho_n+1
rho_old = rho;
// Compute new rho
rho = ublas::inner_prod(_r, _rstar);
if( fabs(rho) < 1e-25 )
{
dolfin_error("uBLASKrylovSolver.h",
"solve linear system using uBLAS BiCGStab solver",
"Solution failed to converge, rho = %g", rho);
}
beta = (rho/rho_old)*(alpha/omega);
// p = r1 + beta*p - beta*omega*A*p
p *= beta;
noalias(_p) += _r - beta*omega*_v;
// My = p
_pc->solve(y, p);
// v = A*y
//axpy_prod(A, y, v, true);
A.mult(y, v);
// alpha = (r, rstart) / (v, rstar)
alpha = rho/ublas::inner_prod(_v, _rstar);
// s = r - alpha*v
noalias(_s) = _r - alpha*_v;
// Mz = s
_pc->solve(z, s);
// t = A*z
//axpy_prod(A, z, t, true);
A.mult(z, t);
// omega = (t, s) / (t,t)
omega = ublas::inner_prod(_t, _s)/ublas::inner_prod(_t, _t);
// x = x + alpha*p + omega*s
noalias(_x) += alpha*_y + omega*_z;
// r = s - omega*t
noalias(_r) = _s - omega*_t;
// Compute norm of the residual and check for convergence
r_norm = norm_2(_r);
if( r_norm/r0_norm < rtol || r_norm < atol)
converged = true;
++iteration;
}
return iteration;
}
//-----------------------------------------------------------------------------
}
#endif
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