int
gsl_linalg_LQ_vecQ (const gsl_matrix * LQ, const gsl_vector * tau, gsl_vector * v)
{
const size_t N = LQ->size1;
const size_t M = LQ->size2;
if (tau->size != GSL_MIN (M, N))
{
GSL_ERROR ("size of tau must be MIN(M,N)", GSL_EBADLEN);
}
else if (v->size != M)
{
GSL_ERROR ("vector size must be M", GSL_EBADLEN);
}
else
{
size_t i;
/* compute v Q^T */
for (i = GSL_MIN (M, N); i-- > 0;)
{
gsl_vector_const_view c = gsl_matrix_const_row (LQ, i);
gsl_vector_const_view h = gsl_vector_const_subvector (&(c.vector),
i, M - i);
gsl_vector_view w = gsl_vector_subvector (v, i, M - i);
double ti = gsl_vector_get (tau, i);
gsl_linalg_householder_hv (ti, &(h.vector), &(w.vector));
}
return GSL_SUCCESS;
}
}
int
gsl_linalg_QR_Qvec (const gsl_matrix * QR, const gsl_vector * tau, gsl_vector * v)
{
const size_t M = QR->size1;
const size_t N = QR->size2;
if (tau->size != GSL_MIN (M, N))
{
GSL_ERROR ("size of tau must be MIN(M,N)", GSL_EBADLEN);
}
else if (v->size != M)
{
GSL_ERROR ("vector size must be M", GSL_EBADLEN);
}
else
{
size_t i;
/* compute Q^T v */
for (i = GSL_MIN (M, N); i-- > 0;)
{
gsl_vector_const_view c = gsl_matrix_const_column (QR, i);
gsl_vector_const_view h = gsl_vector_const_subvector (&(c.vector),
i, M - i);
gsl_vector_view w = gsl_vector_subvector (v, i, M - i);
double ti = gsl_vector_get (tau, i);
gsl_linalg_householder_hv (ti, &h.vector, &w.vector);
}
return GSL_SUCCESS;
}
}
/**
* C++ version of gsl_linalg_householder_hv().
* @param tau A scalar
* @param v A vector
* @param w Another vector
* @return Error code on failure
*/
inline int householder_hv( double tau, vector const& v, vector& w ){
return gsl_linalg_householder_hv( tau, v.get(), w.get() ); }
void Vector::householderTransform ( const double tau, const Vector& householder ) {
gsl_linalg_householder_hv( tau, &householder.vector, &vector );
}
static int
gmres_iterate(const gsl_spmatrix *A, const gsl_vector *b,
const double tol, gsl_vector *x,
void *vstate)
{
const size_t N = A->size1;
gmres_state_t *state = (gmres_state_t *) vstate;
if (N != A->size2)
{
GSL_ERROR("matrix must be square", GSL_ENOTSQR);
}
else if (N != b->size)
{
GSL_ERROR("matrix does not match right hand side", GSL_EBADLEN);
}
else if (N != x->size)
{
GSL_ERROR("matrix does not match solution vector", GSL_EBADLEN);
}
else if (N != state->n)
{
GSL_ERROR("matrix does not match workspace", GSL_EBADLEN);
}
else
{
int status = GSL_SUCCESS;
const size_t maxit = state->m;
const double normb = gsl_blas_dnrm2(b); /* ||b|| */
const double reltol = tol * normb; /* tol*||b|| */
double normr; /* ||r|| */
size_t m, k;
double tau; /* householder scalar */
gsl_matrix *H = state->H; /* Hessenberg matrix */
gsl_vector *r = state->r; /* residual vector */
gsl_vector *w = state->y; /* least squares RHS */
gsl_matrix_view Rm; /* R_m = H(1:m,2:m+1) */
gsl_vector_view ym; /* y(1:m) */
gsl_vector_view h0 = gsl_matrix_column(H, 0);
/*
* The Hessenberg matrix will have the following structure:
*
* H = [ ||r_0|| | v_1 v_2 ... v_m ]
* [ u_1 | u_2 u_3 ... u_{m+1} ]
*
* where v_j are the orthonormal vectors spanning the Krylov
* subpsace of length j + 1 and u_{j+1} are the householder
* vectors of length n - j - 1.
* In fact, u_{j+1} has length n - j since u_{j+1}[0] = 1,
* but this 1 is not stored.
*/
gsl_matrix_set_zero(H);
/* Step 1a: compute r = b - A*x_0 */
gsl_vector_memcpy(r, b);
gsl_spblas_dgemv(CblasNoTrans, -1.0, A, x, 1.0, r);
/* Step 1b */
gsl_vector_memcpy(&h0.vector, r);
tau = gsl_linalg_householder_transform(&h0.vector);
/* store tau_1 */
gsl_vector_set(state->tau, 0, tau);
/* initialize w (stored in state->y) */
gsl_vector_set_zero(w);
gsl_vector_set(w, 0, gsl_vector_get(&h0.vector, 0));
for (m = 1; m <= maxit; ++m)
{
size_t j = m - 1; /* C indexing */
double c, s; /* Givens rotation */
/* v_m */
gsl_vector_view vm = gsl_matrix_column(H, m);
/* v_m(m:end) */
gsl_vector_view vv = gsl_vector_subvector(&vm.vector, j, N - j);
/* householder vector u_m for projection P_m */
gsl_vector_view um = gsl_matrix_subcolumn(H, j, j, N - j);
/* Step 2a: form v_m = P_m e_m = e_m - tau_m w_m */
gsl_vector_set_zero(&vm.vector);
gsl_vector_memcpy(&vv.vector, &um.vector);
tau = gsl_vector_get(state->tau, j); /* tau_m */
gsl_vector_scale(&vv.vector, -tau);
gsl_vector_set(&vv.vector, 0, 1.0 - tau);
/* Step 2a: v_m <- P_1 P_2 ... P_{m-1} v_m */
for (k = j; k > 0 && k--; )
{
gsl_vector_view uk =
gsl_matrix_subcolumn(H, k, k, N - k);
gsl_vector_view vk =
gsl_vector_subvector(&vm.vector, k, N - k);
tau = gsl_vector_get(state->tau, k);
gsl_linalg_householder_hv(tau, &uk.vector, &vk.vector);
}
/* Step 2a: v_m <- A*v_m */
gsl_spblas_dgemv(CblasNoTrans, 1.0, A, &vm.vector, 0.0, r);
gsl_vector_memcpy(&vm.vector, r);
/* Step 2a: v_m <- P_m ... P_1 v_m */
for (k = 0; k <= j; ++k)
{
gsl_vector_view uk = gsl_matrix_subcolumn(H, k, k, N - k);
gsl_vector_view vk = gsl_vector_subvector(&vm.vector, k, N - k);
tau = gsl_vector_get(state->tau, k);
gsl_linalg_householder_hv(tau, &uk.vector, &vk.vector);
}
/* Steps 2c,2d: find P_{m+1} and set v_m <- P_{m+1} v_m */
if (m < N)
{
/* householder vector u_{m+1} for projection P_{m+1} */
gsl_vector_view ump1 = gsl_matrix_subcolumn(H, m, m, N - m);
tau = gsl_linalg_householder_transform(&ump1.vector);
gsl_vector_set(state->tau, j + 1, tau);
}
/* Step 2e: v_m <- J_{m-1} ... J_1 v_m */
for (k = 0; k < j; ++k)
{
gsl_linalg_givens_gv(&vm.vector, k, k + 1,
state->c[k], state->s[k]);
}
if (m < N)
{
/* Step 2g: find givens rotation J_m for v_m(m:m+1) */
gsl_linalg_givens(gsl_vector_get(&vm.vector, j),
gsl_vector_get(&vm.vector, j + 1),
&c, &s);
/* store givens rotation for later use */
state->c[j] = c;
state->s[j] = s;
/* Step 2h: v_m <- J_m v_m */
gsl_linalg_givens_gv(&vm.vector, j, j + 1, c, s);
/* Step 2h: w <- J_m w */
gsl_linalg_givens_gv(w, j, j + 1, c, s);
}
/*
* Step 2i: R_m = [ R_{m-1}, v_m ] - already taken care
* of due to our memory storage scheme
*/
/* Step 2j: check residual w_{m+1} for convergence */
normr = fabs(gsl_vector_get(w, j + 1));
if (normr <= reltol)
{
/*
* method has converged, break out of loop to compute
* update to solution vector x
*/
break;
}
}
/*
* At this point, we have either converged to a solution or
* completed all maxit iterations. In either case, compute
* an update to the solution vector x and test again for
* convergence.
*/
/* rewind m if we exceeded maxit iterations */
if (m > maxit)
m--;
/* Step 3a: solve triangular system R_m y_m = w, in place */
Rm = gsl_matrix_submatrix(H, 0, 1, m, m);
ym = gsl_vector_subvector(w, 0, m);
gsl_blas_dtrsv(CblasUpper, CblasNoTrans, CblasNonUnit,
&Rm.matrix, &ym.vector);
/*
* Step 3b: update solution vector x; the loop below
* uses a different but equivalent formulation from
* Saad, algorithm 6.10, step 14; store Krylov projection
* V_m y_m in 'r'
*/
gsl_vector_set_zero(r);
for (k = m; k > 0 && k--; )
{
double ymk = gsl_vector_get(&ym.vector, k);
gsl_vector_view uk = gsl_matrix_subcolumn(H, k, k, N - k);
gsl_vector_view rk = gsl_vector_subvector(r, k, N - k);
/* r <- n_k e_k + r */
gsl_vector_set(r, k, gsl_vector_get(r, k) + ymk);
/* r <- P_k r */
tau = gsl_vector_get(state->tau, k);
gsl_linalg_householder_hv(tau, &uk.vector, &rk.vector);
}
/* x <- x + V_m y_m */
gsl_vector_add(x, r);
/* compute new residual r = b - A*x */
gsl_vector_memcpy(r, b);
gsl_spblas_dgemv(CblasNoTrans, -1.0, A, x, 1.0, r);
normr = gsl_blas_dnrm2(r);
if (normr <= reltol)
status = GSL_SUCCESS; /* converged */
else
status = GSL_CONTINUE; /* not yet converged */
/* store residual norm */
state->normr = normr;
return status;
}
} /* gmres_iterate() */
