int
gsl_linalg_symmtd_decomp (gsl_matrix * A, gsl_vector * tau)
{
if (A->size1 != A->size2)
{
GSL_ERROR ("symmetric tridiagonal decomposition requires square matrix",
GSL_ENOTSQR);
}
else if (tau->size + 1 != A->size1)
{
GSL_ERROR ("size of tau must be (matrix size - 1)", GSL_EBADLEN);
}
else
{
const size_t N = A->size1;
size_t i;
for (i = 0 ; i < N - 2; i++)
{
gsl_vector_view c = gsl_matrix_column (A, i);
gsl_vector_view v = gsl_vector_subvector (&c.vector, i + 1, N - (i + 1));
double tau_i = gsl_linalg_householder_transform (&v.vector);
/* Apply the transformation H^T A H to the remaining columns */
if (tau_i != 0.0)
{
gsl_matrix_view m = gsl_matrix_submatrix (A, i + 1, i + 1,
N - (i+1), N - (i+1));
double ei = gsl_vector_get(&v.vector, 0);
gsl_vector_view x = gsl_vector_subvector (tau, i, N-(i+1));
gsl_vector_set (&v.vector, 0, 1.0);
/* x = tau * A * v */
gsl_blas_dsymv (CblasLower, tau_i, &m.matrix, &v.vector, 0.0, &x.vector);
/* w = x - (1/2) tau * (x' * v) * v */
{
double xv, alpha;
gsl_blas_ddot(&x.vector, &v.vector, &xv);
alpha = - (tau_i / 2.0) * xv;
gsl_blas_daxpy(alpha, &v.vector, &x.vector);
}
/* apply the transformation A = A - v w' - w v' */
gsl_blas_dsyr2(CblasLower, -1.0, &v.vector, &x.vector, &m.matrix);
gsl_vector_set (&v.vector, 0, ei);
}
gsl_vector_set (tau, i, tau_i);
}
return GSL_SUCCESS;
}
}
int
gsl_linalg_LQ_decomp (gsl_matrix * A, gsl_vector * tau)
{
const size_t N = A->size1;
const size_t M = A->size2;
if (tau->size != GSL_MIN (M, N))
{
GSL_ERROR ("size of tau must be MIN(M,N)", GSL_EBADLEN);
}
else
{
size_t i;
for (i = 0; i < GSL_MIN (M, N); i++)
{
/* Compute the Householder transformation to reduce the j-th
column of the matrix to a multiple of the j-th unit vector */
gsl_vector_view c_full = gsl_matrix_row (A, i);
gsl_vector_view c = gsl_vector_subvector (&(c_full.vector), i, M-i);
double tau_i = gsl_linalg_householder_transform (&(c.vector));
gsl_vector_set (tau, i, tau_i);
/* Apply the transformation to the remaining columns and
update the norms */
if (i + 1 < N)
{
gsl_matrix_view m = gsl_matrix_submatrix (A, i + 1, i, N - (i + 1), M - i );
gsl_linalg_householder_mh (tau_i, &(c.vector), &(m.matrix));
}
}
return GSL_SUCCESS;
}
}
int
gsl_linalg_PTLQ_decomp (gsl_matrix * A, gsl_vector * tau, gsl_permutation * p, int *signum, gsl_vector * norm)
{
const size_t N = A->size1;
const size_t M = A->size2;
if (tau->size != GSL_MIN (M, N))
{
GSL_ERROR ("size of tau must be MIN(M,N)", GSL_EBADLEN);
}
else if (p->size != N)
{
GSL_ERROR ("permutation size must be N", GSL_EBADLEN);
}
else if (norm->size != N)
{
GSL_ERROR ("norm size must be N", GSL_EBADLEN);
}
else
{
size_t i;
*signum = 1;
gsl_permutation_init (p); /* set to identity */
/* Compute column norms and store in workspace */
for (i = 0; i < N; i++)
{
gsl_vector_view c = gsl_matrix_row (A, i);
double x = gsl_blas_dnrm2 (&c.vector);
gsl_vector_set (norm, i, x);
}
for (i = 0; i < GSL_MIN (M, N); i++)
{
/* Bring the column of largest norm into the pivot position */
double max_norm = gsl_vector_get(norm, i);
size_t j, kmax = i;
for (j = i + 1; j < N; j++)
{
double x = gsl_vector_get (norm, j);
if (x > max_norm)
{
max_norm = x;
kmax = j;
}
}
if (kmax != i)
{
gsl_matrix_swap_rows (A, i, kmax);
gsl_permutation_swap (p, i, kmax);
gsl_vector_swap_elements(norm,i,kmax);
(*signum) = -(*signum);
}
/* Compute the Householder transformation to reduce the j-th
column of the matrix to a multiple of the j-th unit vector */
{
gsl_vector_view c_full = gsl_matrix_row (A, i);
gsl_vector_view c = gsl_vector_subvector (&c_full.vector,
i, M - i);
double tau_i = gsl_linalg_householder_transform (&c.vector);
gsl_vector_set (tau, i, tau_i);
/* Apply the transformation to the remaining columns */
if (i + 1 < N)
{
gsl_matrix_view m = gsl_matrix_submatrix (A, i +1, i, N - (i+1), M - i);
gsl_linalg_householder_mh (tau_i, &c.vector, &m.matrix);
}
}
/* Update the norms of the remaining columns too */
if (i + 1 < M)
{
for (j = i + 1; j < N; j++)
{
double x = gsl_vector_get (norm, j);
if (x > 0.0)
{
double y = 0;
double temp= gsl_matrix_get (A, j, i) / x;
if (fabs (temp) >= 1)
y = 0.0;
else
y = x * sqrt (1 - temp * temp);
/* recompute norm to prevent loss of accuracy */
if (fabs (y / x) < sqrt (20.0) * GSL_SQRT_DBL_EPSILON)
{
gsl_vector_view c_full = gsl_matrix_row (A, j);
gsl_vector_view c =
gsl_vector_subvector(&c_full.vector,
i+1, M - (i+1));
y = gsl_blas_dnrm2 (&c.vector);
}
gsl_vector_set (norm, j, y);
}
}
}
}
return GSL_SUCCESS;
}
}
/**
* C++ version of gsl_linalg_householder_transform().
* @param v A vector
* @return The Householder transform
*/
inline double householder_transform( vector& v ){
return gsl_linalg_householder_transform( v.get() ); }
double Vector::householderize () {
const size_t dims = countDimensions();
return 0 < dims ? gsl_linalg_householder_transform( &vector ) : nan( "unspecified" );
}
int
gsl_linalg_hessenberg_decomp(gsl_matrix *A, gsl_vector *tau)
{
const size_t N = A->size1;
if (N != A->size2)
{
GSL_ERROR ("Hessenberg reduction requires square matrix",
GSL_ENOTSQR);
}
else if (N != tau->size)
{
GSL_ERROR ("tau vector must match matrix size", GSL_EBADLEN);
}
else if (N < 3)
{
/* nothing to do */
return GSL_SUCCESS;
}
else
{
size_t i; /* looping */
gsl_vector_view c, /* matrix column */
hv; /* householder vector */
gsl_matrix_view m;
double tau_i; /* beta in algorithm 7.4.2 */
for (i = 0; i < N - 2; ++i)
{
/*
* make a copy of A(i + 1:n, i) and store it in the section
* of 'tau' that we haven't stored coefficients in yet
*/
c = gsl_matrix_subcolumn(A, i, i + 1, N - i - 1);
hv = gsl_vector_subvector(tau, i + 1, N - (i + 1));
gsl_vector_memcpy(&hv.vector, &c.vector);
/* compute householder transformation of A(i+1:n,i) */
tau_i = gsl_linalg_householder_transform(&hv.vector);
/* apply left householder matrix (I - tau_i v v') to A */
m = gsl_matrix_submatrix(A, i + 1, i, N - (i + 1), N - i);
gsl_linalg_householder_hm(tau_i, &hv.vector, &m.matrix);
/* apply right householder matrix (I - tau_i v v') to A */
m = gsl_matrix_submatrix(A, 0, i + 1, N, N - (i + 1));
gsl_linalg_householder_mh(tau_i, &hv.vector, &m.matrix);
/* save Householder coefficient */
gsl_vector_set(tau, i, tau_i);
/*
* store Householder vector below the subdiagonal in column
* i of the matrix. hv(1) does not need to be stored since
* it is always 1.
*/
c = gsl_vector_subvector(&c.vector, 1, c.vector.size - 1);
hv = gsl_vector_subvector(&hv.vector, 1, hv.vector.size - 1);
gsl_vector_memcpy(&c.vector, &hv.vector);
}
return GSL_SUCCESS;
}
} /* gsl_linalg_hessenberg_decomp() */
int
gsl_linalg_SV_decomp_mod (gsl_matrix * A,
gsl_matrix * X,
gsl_matrix * V, gsl_vector * S, gsl_vector * work)
{
size_t i, j;
const size_t M = A->size1;
const size_t N = A->size2;
if (M < N)
{
GSL_ERROR ("svd of MxN matrix, M<N, is not implemented", GSL_EUNIMPL);
}
else if (V->size1 != N)
{
GSL_ERROR ("square matrix V must match second dimension of matrix A",
GSL_EBADLEN);
}
else if (V->size1 != V->size2)
{
GSL_ERROR ("matrix V must be square", GSL_ENOTSQR);
}
else if (X->size1 != N)
{
GSL_ERROR ("square matrix X must match second dimension of matrix A",
GSL_EBADLEN);
}
else if (X->size1 != X->size2)
{
GSL_ERROR ("matrix X must be square", GSL_ENOTSQR);
}
else if (S->size != N)
{
GSL_ERROR ("length of vector S must match second dimension of matrix A",
GSL_EBADLEN);
}
else if (work->size != N)
{
GSL_ERROR ("length of workspace must match second dimension of matrix A",
GSL_EBADLEN);
}
if (N == 1)
{
gsl_vector_view column = gsl_matrix_column (A, 0);
double norm = gsl_blas_dnrm2 (&column.vector);
gsl_vector_set (S, 0, norm);
gsl_matrix_set (V, 0, 0, 1.0);
if (norm != 0.0)
{
gsl_blas_dscal (1.0/norm, &column.vector);
}
return GSL_SUCCESS;
}
/* Convert A into an upper triangular matrix R */
for (i = 0; i < N; i++)
{
gsl_vector_view c = gsl_matrix_column (A, i);
gsl_vector_view v = gsl_vector_subvector (&c.vector, i, M - i);
double tau_i = gsl_linalg_householder_transform (&v.vector);
/* Apply the transformation to the remaining columns */
if (i + 1 < N)
{
gsl_matrix_view m =
gsl_matrix_submatrix (A, i, i + 1, M - i, N - (i + 1));
gsl_linalg_householder_hm (tau_i, &v.vector, &m.matrix);
}
gsl_vector_set (S, i, tau_i);
}
/* Copy the upper triangular part of A into X */
for (i = 0; i < N; i++)
{
for (j = 0; j < i; j++)
{
gsl_matrix_set (X, i, j, 0.0);
}
{
double Aii = gsl_matrix_get (A, i, i);
gsl_matrix_set (X, i, i, Aii);
}
for (j = i + 1; j < N; j++)
{
double Aij = gsl_matrix_get (A, i, j);
gsl_matrix_set (X, i, j, Aij);
}
}
/* Convert A into an orthogonal matrix L */
for (j = N; j-- > 0;)
{
/* Householder column transformation to accumulate L */
double tj = gsl_vector_get (S, j);
gsl_matrix_view m = gsl_matrix_submatrix (A, j, j, M - j, N - j);
gsl_linalg_householder_hm1 (tj, &m.matrix);
}
/* unpack R into X V S */
gsl_linalg_SV_decomp (X, V, S, work);
/* Multiply L by X, to obtain U = L X, stored in U */
{
gsl_vector_view sum = gsl_vector_subvector (work, 0, N);
for (i = 0; i < M; i++)
{
gsl_vector_view L_i = gsl_matrix_row (A, i);
gsl_vector_set_zero (&sum.vector);
for (j = 0; j < N; j++)
{
double Lij = gsl_vector_get (&L_i.vector, j);
gsl_vector_view X_j = gsl_matrix_row (X, j);
gsl_blas_daxpy (Lij, &X_j.vector, &sum.vector);
}
gsl_vector_memcpy (&L_i.vector, &sum.vector);
}
}
return GSL_SUCCESS;
}
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() */
