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() */