/* luf.c (sparse LU-factorization) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * Copyright (C) 2012-2013 Free Software Foundation, Inc. * Written by Andrew Makhorin . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK 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 General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "env.h" #include "luf.h" /*********************************************************************** * luf_store_v_cols - store matrix V = A in column-wise format * * This routine stores matrix V = A in column-wise format, where A is * the original matrix to be factorized. * * On exit the routine returns the number of non-zeros in matrix V. */ int luf_store_v_cols(LUF *luf, int (*col)(void *info, int j, int ind[], double val[]), void *info, int ind[], double val[]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int vc_ref = luf->vc_ref; int *vc_ptr = &sva->ptr[vc_ref-1]; int *vc_len = &sva->len[vc_ref-1]; int *vc_cap = &sva->cap[vc_ref-1]; int j, len, ptr, nnz; nnz = 0; for (j = 1; j <= n; j++) { /* get j-th column */ len = col(info, j, ind, val); xassert(0 <= len && len <= n); /* enlarge j-th column capacity */ if (vc_cap[j] < len) { if (sva->r_ptr - sva->m_ptr < len) { sva_more_space(sva, len); sv_ind = sva->ind; sv_val = sva->val; } sva_enlarge_cap(sva, vc_ref-1+j, len, 0); } /* store j-th column */ ptr = vc_ptr[j]; memcpy(&sv_ind[ptr], &ind[1], len * sizeof(int)); memcpy(&sv_val[ptr], &val[1], len * sizeof(double)); vc_len[j] = len; nnz += len; } return nnz; } /*********************************************************************** * luf_check_all - check LU-factorization before k-th elimination step * * This routine checks that before performing k-th elimination step, * 1 <= k <= n+1, all components of the LU-factorization are correct. * * In case of k = n+1, i.e. after last elimination step, it is assumed * that rows of F and columns of V are *not* built yet. * * NOTE: For testing/debugging only. */ void luf_check_all(LUF *luf, int k) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int fr_ref = luf->fr_ref; int *fr_len = &sva->len[fr_ref-1]; int fc_ref = luf->fc_ref; int *fc_ptr = &sva->ptr[fc_ref-1]; int *fc_len = &sva->len[fc_ref-1]; int vr_ref = luf->vr_ref; int *vr_ptr = &sva->ptr[vr_ref-1]; int *vr_len = &sva->len[vr_ref-1]; int vc_ref = luf->vc_ref; int *vc_ptr = &sva->ptr[vc_ref-1]; int *vc_len = &sva->len[vc_ref-1]; int *pp_ind = luf->pp_ind; int *pp_inv = luf->pp_inv; int *qq_ind = luf->qq_ind; int *qq_inv = luf->qq_inv; int i, ii, i_ptr, i_end, j, jj, j_ptr, j_end; xassert(n > 0); xassert(1 <= k && k <= n+1); /* check permutation matrix P */ for (i = 1; i <= n; i++) { ii = pp_ind[i]; xassert(1 <= ii && ii <= n); xassert(pp_inv[ii] == i); } /* check permutation matrix Q */ for (j = 1; j <= n; j++) { jj = qq_inv[j]; xassert(1 <= jj && jj <= n); xassert(qq_ind[jj] == j); } /* check row-wise representation of matrix F */ for (i = 1; i <= n; i++) xassert(fr_len[i] == 0); /* check column-wise representation of matrix F */ for (j = 1; j <= n; j++) { /* j-th column of F = jj-th column of L */ jj = pp_ind[j]; if (jj < k) { j_ptr = fc_ptr[j]; j_end = j_ptr + fc_len[j]; for (; j_ptr < j_end; j_ptr++) { i = sv_ind[j_ptr]; xassert(1 <= i && i <= n); ii = pp_ind[i]; /* f[i,j] = l[ii,jj] */ xassert(ii > jj); xassert(sv_val[j_ptr] != 0.0); } } else /* jj >= k */ xassert(fc_len[j] == 0); } /* check row-wise representation of matrix V */ for (i = 1; i <= n; i++) { /* i-th row of V = ii-th row of U */ ii = pp_ind[i]; i_ptr = vr_ptr[i]; i_end = i_ptr + vr_len[i]; for (; i_ptr < i_end; i_ptr++) { j = sv_ind[i_ptr]; xassert(1 <= j && j <= n); jj = qq_inv[j]; /* v[i,j] = u[ii,jj] */ if (ii < k) xassert(jj > ii); else /* ii >= k */ { xassert(jj >= k); /* find v[i,j] in j-th column */ j_ptr = vc_ptr[j]; j_end = j_ptr + vc_len[j]; for (; sv_ind[j_ptr] != i; j_ptr++) /* nop */; xassert(j_ptr < j_end); } xassert(sv_val[i_ptr] != 0.0); } } /* check column-wise representation of matrix V */ for (j = 1; j <= n; j++) { /* j-th column of V = jj-th column of U */ jj = qq_inv[j]; if (jj < k) xassert(vc_len[j] == 0); else /* jj >= k */ { j_ptr = vc_ptr[j]; j_end = j_ptr + vc_len[j]; for (; j_ptr < j_end; j_ptr++) { i = sv_ind[j_ptr]; ii = pp_ind[i]; /* v[i,j] = u[ii,jj] */ xassert(ii >= k); /* find v[i,j] in i-th row */ i_ptr = vr_ptr[i]; i_end = i_ptr + vr_len[i]; for (; sv_ind[i_ptr] != j; i_ptr++) /* nop */; xassert(i_ptr < i_end); } } } return; } /*********************************************************************** * luf_build_v_rows - build matrix V in row-wise format * * This routine builds the row-wise representation of matrix V in the * left part of SVA using its column-wise representation. * * NOTE: On entry to the routine all rows of matrix V should have zero * capacity. * * The working array len should have at least 1+n elements (len[0] is * not used). */ void luf_build_v_rows(LUF *luf, int len[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int vr_ref = luf->vr_ref; int *vr_ptr = &sva->ptr[vr_ref-1]; int *vr_len = &sva->len[vr_ref-1]; int vc_ref = luf->vc_ref; int *vc_ptr = &sva->ptr[vc_ref-1]; int *vc_len = &sva->len[vc_ref-1]; int i, j, end, nnz, ptr, ptr1; /* calculate the number of non-zeros in each row of matrix V and * the total number of non-zeros */ nnz = 0; for (i = 1; i <= n; i++) len[i] = 0; for (j = 1; j <= n; j++) { nnz += vc_len[j]; for (end = (ptr = vc_ptr[j]) + vc_len[j]; ptr < end; ptr++) len[sv_ind[ptr]]++; } /* we need at least nnz free locations in SVA */ if (sva->r_ptr - sva->m_ptr < nnz) { sva_more_space(sva, nnz); sv_ind = sva->ind; sv_val = sva->val; } /* reserve locations for rows of matrix V */ for (i = 1; i <= n; i++) { if (len[i] > 0) sva_enlarge_cap(sva, vr_ref-1+i, len[i], 0); vr_len[i] = len[i]; } /* walk thru column of matrix V and build its rows */ for (j = 1; j <= n; j++) { for (end = (ptr = vc_ptr[j]) + vc_len[j]; ptr < end; ptr++) { i = sv_ind[ptr]; sv_ind[ptr1 = vr_ptr[i] + (--len[i])] = j; sv_val[ptr1] = sv_val[ptr]; } } return; } /*********************************************************************** * luf_build_f_rows - build matrix F in row-wise format * * This routine builds the row-wise representation of matrix F in the * right part of SVA using its column-wise representation. * * NOTE: On entry to the routine all rows of matrix F should have zero * capacity. * * The working array len should have at least 1+n elements (len[0] is * not used). */ void luf_build_f_rows(LUF *luf, int len[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int fr_ref = luf->fr_ref; int *fr_ptr = &sva->ptr[fr_ref-1]; int *fr_len = &sva->len[fr_ref-1]; int fc_ref = luf->fc_ref; int *fc_ptr = &sva->ptr[fc_ref-1]; int *fc_len = &sva->len[fc_ref-1]; int i, j, end, nnz, ptr, ptr1; /* calculate the number of non-zeros in each row of matrix F and * the total number of non-zeros (except diagonal elements) */ nnz = 0; for (i = 1; i <= n; i++) len[i] = 0; for (j = 1; j <= n; j++) { nnz += fc_len[j]; for (end = (ptr = fc_ptr[j]) + fc_len[j]; ptr < end; ptr++) len[sv_ind[ptr]]++; } /* we need at least nnz free locations in SVA */ if (sva->r_ptr - sva->m_ptr < nnz) { sva_more_space(sva, nnz); sv_ind = sva->ind; sv_val = sva->val; } /* reserve locations for rows of matrix F */ for (i = 1; i <= n; i++) { if (len[i] > 0) sva_reserve_cap(sva, fr_ref-1+i, len[i]); fr_len[i] = len[i]; } /* walk through columns of matrix F and build its rows */ for (j = 1; j <= n; j++) { for (end = (ptr = fc_ptr[j]) + fc_len[j]; ptr < end; ptr++) { i = sv_ind[ptr]; sv_ind[ptr1 = fr_ptr[i] + (--len[i])] = j; sv_val[ptr1] = sv_val[ptr]; } } return; } /*********************************************************************** * luf_build_v_cols - build matrix V in column-wise format * * This routine builds the column-wise representation of matrix V in * the left (if the flag updat is set) or right (if the flag updat is * clear) part of SVA using its row-wise representation. * * NOTE: On entry to the routine all columns of matrix V should have * zero capacity. * * The working array len should have at least 1+n elements (len[0] is * not used). */ void luf_build_v_cols(LUF *luf, int updat, int len[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int vr_ref = luf->vr_ref; int *vr_ptr = &sva->ptr[vr_ref-1]; int *vr_len = &sva->len[vr_ref-1]; int vc_ref = luf->vc_ref; int *vc_ptr = &sva->ptr[vc_ref-1]; int *vc_len = &sva->len[vc_ref-1]; int i, j, end, nnz, ptr, ptr1; /* calculate the number of non-zeros in each column of matrix V * and the total number of non-zeros (except pivot elements) */ nnz = 0; for (j = 1; j <= n; j++) len[j] = 0; for (i = 1; i <= n; i++) { nnz += vr_len[i]; for (end = (ptr = vr_ptr[i]) + vr_len[i]; ptr < end; ptr++) len[sv_ind[ptr]]++; } /* we need at least nnz free locations in SVA */ if (sva->r_ptr - sva->m_ptr < nnz) { sva_more_space(sva, nnz); sv_ind = sva->ind; sv_val = sva->val; } /* reserve locations for columns of matrix V */ for (j = 1; j <= n; j++) { if (len[j] > 0) { if (updat) sva_enlarge_cap(sva, vc_ref-1+j, len[j], 0); else sva_reserve_cap(sva, vc_ref-1+j, len[j]); } vc_len[j] = len[j]; } /* walk through rows of matrix V and build its columns */ for (i = 1; i <= n; i++) { for (end = (ptr = vr_ptr[i]) + vr_len[i]; ptr < end; ptr++) { j = sv_ind[ptr]; sv_ind[ptr1 = vc_ptr[j] + (--len[j])] = i; sv_val[ptr1] = sv_val[ptr]; } } return; } /*********************************************************************** * luf_check_f_rc - check rows and columns of matrix F * * This routine checks that the row- and column-wise representations * of matrix F are identical. * * NOTE: For testing/debugging only. */ void luf_check_f_rc(LUF *luf) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int fr_ref = luf->fr_ref; int *fr_ptr = &sva->ptr[fr_ref-1]; int *fr_len = &sva->len[fr_ref-1]; int fc_ref = luf->fc_ref; int *fc_ptr = &sva->ptr[fc_ref-1]; int *fc_len = &sva->len[fc_ref-1]; int i, i_end, i_ptr, j, j_end, j_ptr; /* walk thru rows of matrix F */ for (i = 1; i <= n; i++) { for (i_end = (i_ptr = fr_ptr[i]) + fr_len[i]; i_ptr < i_end; i_ptr++) { j = sv_ind[i_ptr]; /* find element f[i,j] in j-th column of matrix F */ for (j_end = (j_ptr = fc_ptr[j]) + fc_len[j]; sv_ind[j_ptr] != i; j_ptr++) /* nop */; xassert(j_ptr < j_end); xassert(sv_val[i_ptr] == sv_val[j_ptr]); /* mark element f[i,j] */ sv_ind[j_ptr] = -i; } } /* walk thru column of matix F and check that all elements has been marked */ for (j = 1; j <= n; j++) { for (j_end = (j_ptr = fc_ptr[j]) + fc_len[j]; j_ptr < j_end; j_ptr++) { xassert((i = sv_ind[j_ptr]) < 0); /* unmark element f[i,j] */ sv_ind[j_ptr] = -i; } } return; } /*********************************************************************** * luf_check_v_rc - check rows and columns of matrix V * * This routine checks that the row- and column-wise representations * of matrix V are identical. * * NOTE: For testing/debugging only. */ void luf_check_v_rc(LUF *luf) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int vr_ref = luf->vr_ref; int *vr_ptr = &sva->ptr[vr_ref-1]; int *vr_len = &sva->len[vr_ref-1]; int vc_ref = luf->vc_ref; int *vc_ptr = &sva->ptr[vc_ref-1]; int *vc_len = &sva->len[vc_ref-1]; int i, i_end, i_ptr, j, j_end, j_ptr; /* walk thru rows of matrix V */ for (i = 1; i <= n; i++) { for (i_end = (i_ptr = vr_ptr[i]) + vr_len[i]; i_ptr < i_end; i_ptr++) { j = sv_ind[i_ptr]; /* find element v[i,j] in j-th column of matrix V */ for (j_end = (j_ptr = vc_ptr[j]) + vc_len[j]; sv_ind[j_ptr] != i; j_ptr++) /* nop */; xassert(j_ptr < j_end); xassert(sv_val[i_ptr] == sv_val[j_ptr]); /* mark element v[i,j] */ sv_ind[j_ptr] = -i; } } /* walk thru column of matix V and check that all elements has been marked */ for (j = 1; j <= n; j++) { for (j_end = (j_ptr = vc_ptr[j]) + vc_len[j]; j_ptr < j_end; j_ptr++) { xassert((i = sv_ind[j_ptr]) < 0); /* unmark element v[i,j] */ sv_ind[j_ptr] = -i; } } return; } /*********************************************************************** * luf_f_solve - solve system F * x = b * * This routine solves the system F * x = b, where the matrix F is the * left factor of the sparse LU-factorization. * * On entry the array x should contain elements of the right-hand side * vector b in locations x[1], ..., x[n], where n is the order of the * matrix F. On exit this array will contain elements of the solution * vector x in the same locations. */ void luf_f_solve(LUF *luf, double x[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int fc_ref = luf->fc_ref; int *fc_ptr = &sva->ptr[fc_ref-1]; int *fc_len = &sva->len[fc_ref-1]; int *pp_inv = luf->pp_inv; int j, k, ptr, end; double x_j; for (k = 1; k <= n; k++) { /* k-th column of L = j-th column of F */ j = pp_inv[k]; /* x[j] is already computed */ /* walk thru j-th column of matrix F and substitute x[j] into * other equations */ if ((x_j = x[j]) != 0.0) { for (end = (ptr = fc_ptr[j]) + fc_len[j]; ptr < end; ptr++) x[sv_ind[ptr]] -= sv_val[ptr] * x_j; } } return; } /*********************************************************************** * luf_ft_solve - solve system F' * x = b * * This routine solves the system F' * x = b, where F' is a matrix * transposed to the matrix F, which is the left factor of the sparse * LU-factorization. * * On entry the array x should contain elements of the right-hand side * vector b in locations x[1], ..., x[n], where n is the order of the * matrix F. On exit this array will contain elements of the solution * vector x in the same locations. */ void luf_ft_solve(LUF *luf, double x[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; int fr_ref = luf->fr_ref; int *fr_ptr = &sva->ptr[fr_ref-1]; int *fr_len = &sva->len[fr_ref-1]; int *pp_inv = luf->pp_inv; int i, k, ptr, end; double x_i; for (k = n; k >= 1; k--) { /* k-th column of L' = i-th row of F */ i = pp_inv[k]; /* x[i] is already computed */ /* walk thru i-th row of matrix F and substitute x[i] into * other equations */ if ((x_i = x[i]) != 0.0) { for (end = (ptr = fr_ptr[i]) + fr_len[i]; ptr < end; ptr++) x[sv_ind[ptr]] -= sv_val[ptr] * x_i; } } return; } /*********************************************************************** * luf_v_solve - solve system V * x = b * * This routine solves the system V * x = b, where the matrix V is the * right factor of the sparse LU-factorization. * * On entry the array b should contain elements of the right-hand side * vector b in locations b[1], ..., b[n], where n is the order of the * matrix V. On exit the array x will contain elements of the solution * vector x in locations x[1], ..., x[n]. Note that the array b will be * clobbered on exit. */ void luf_v_solve(LUF *luf, double b[/*1+n*/], double x[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; double *vr_piv = luf->vr_piv; int vc_ref = luf->vc_ref; int *vc_ptr = &sva->ptr[vc_ref-1]; int *vc_len = &sva->len[vc_ref-1]; int *pp_inv = luf->pp_inv; int *qq_ind = luf->qq_ind; int i, j, k, ptr, end; double x_j; for (k = n; k >= 1; k--) { /* k-th row of U = i-th row of V */ /* k-th column of U = j-th column of V */ i = pp_inv[k]; j = qq_ind[k]; /* compute x[j] = b[i] / u[k,k], where u[k,k] = v[i,j]; * walk through j-th column of matrix V and substitute x[j] * into other equations */ if ((x_j = x[j] = b[i] / vr_piv[i]) != 0.0) { for (end = (ptr = vc_ptr[j]) + vc_len[j]; ptr < end; ptr++) b[sv_ind[ptr]] -= sv_val[ptr] * x_j; } } return; } /*********************************************************************** * luf_vt_solve - solve system V' * x = b * * This routine solves the system V' * x = b, where V' is a matrix * transposed to the matrix V, which is the right factor of the sparse * LU-factorization. * * On entry the array b should contain elements of the right-hand side * vector b in locations b[1], ..., b[n], where n is the order of the * matrix V. On exit the array x will contain elements of the solution * vector x in locations x[1], ..., x[n]. Note that the array b will be * clobbered on exit. */ void luf_vt_solve(LUF *luf, double b[/*1+n*/], double x[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; double *vr_piv = luf->vr_piv; int vr_ref = luf->vr_ref; int *vr_ptr = &sva->ptr[vr_ref-1]; int *vr_len = &sva->len[vr_ref-1]; int *pp_inv = luf->pp_inv; int *qq_ind = luf->qq_ind; int i, j, k, ptr, end; double x_i; for (k = 1; k <= n; k++) { /* k-th row of U' = j-th column of V */ /* k-th column of U' = i-th row of V */ i = pp_inv[k]; j = qq_ind[k]; /* compute x[i] = b[j] / u'[k,k], where u'[k,k] = v[i,j]; * walk through i-th row of matrix V and substitute x[i] into * other equations */ if ((x_i = x[i] = b[j] / vr_piv[i]) != 0.0) { for (end = (ptr = vr_ptr[i]) + vr_len[i]; ptr < end; ptr++) b[sv_ind[ptr]] -= sv_val[ptr] * x_i; } } return; } /*********************************************************************** * luf_vt_solve1 - solve system V' * y = e' to cause growth in y * * This routine is a special version of luf_vt_solve. It solves the * system V'* y = e' = e + delta e, where V' is a matrix transposed to * the matrix V, e is the specified right-hand side vector, and delta e * is a vector of +1 and -1 chosen to cause growth in the solution * vector y. * * On entry the array e should contain elements of the right-hand side * vector e in locations e[1], ..., e[n], where n is the order of the * matrix V. On exit the array y will contain elements of the solution * vector y in locations y[1], ..., y[n]. Note that the array e will be * clobbered on exit. */ void luf_vt_solve1(LUF *luf, double e[/*1+n*/], double y[/*1+n*/]) { int n = luf->n; SVA *sva = luf->sva; int *sv_ind = sva->ind; double *sv_val = sva->val; double *vr_piv = luf->vr_piv; int vr_ref = luf->vr_ref; int *vr_ptr = &sva->ptr[vr_ref-1]; int *vr_len = &sva->len[vr_ref-1]; int *pp_inv = luf->pp_inv; int *qq_ind = luf->qq_ind; int i, j, k, ptr, end; double e_j, y_i; for (k = 1; k <= n; k++) { /* k-th row of U' = j-th column of V */ /* k-th column of U' = i-th row of V */ i = pp_inv[k]; j = qq_ind[k]; /* determine e'[j] = e[j] + delta e[j] */ e_j = (e[j] >= 0.0 ? e[j] + 1.0 : e[j] - 1.0); /* compute y[i] = e'[j] / u'[k,k], where u'[k,k] = v[i,j] */ y_i = y[i] = e_j / vr_piv[i]; /* walk through i-th row of matrix V and substitute y[i] into * other equations */ for (end = (ptr = vr_ptr[i]) + vr_len[i]; ptr < end; ptr++) e[sv_ind[ptr]] -= sv_val[ptr] * y_i; } return; } /*********************************************************************** * luf_estimate_norm - estimate 1-norm of inv(A) * * This routine estimates 1-norm of inv(A) by one step of inverse * iteration for the small singular vector as described in [1]. This * involves solving two systems of equations: * * A'* y = e, * * A * z = y, * * where A' is a matrix transposed to A, and e is a vector of +1 and -1 * chosen to cause growth in y. Then * * estimate 1-norm of inv(A) = (1-norm of z) / (1-norm of y) * * REFERENCES * * 1. G.E.Forsythe, M.A.Malcolm, C.B.Moler. Computer Methods for * Mathematical Computations. Prentice-Hall, Englewood Cliffs, N.J., * pp. 30-62 (subroutines DECOMP and SOLVE). */ double luf_estimate_norm(LUF *luf, double w1[/*1+n*/], double w2[/*1+n*/]) { int n = luf->n; double *e = w1; double *y = w2; double *z = w1; int i; double y_norm, z_norm; /* y = inv(A') * e = inv(F') * inv(V') * e */ /* compute y' = inv(V') * e to cause growth in y' */ for (i = 1; i <= n; i++) e[i] = 0.0; luf_vt_solve1(luf, e, y); /* compute y = inv(F') * y' */ luf_ft_solve(luf, y); /* compute 1-norm of y = sum |y[i]| */ y_norm = 0.0; for (i = 1; i <= n; i++) y_norm += (y[i] >= 0.0 ? +y[i] : -y[i]); /* z = inv(A) * y = inv(V) * inv(F) * y */ /* compute z' = inv(F) * y */ luf_f_solve(luf, y); /* compute z = inv(V) * z' */ luf_v_solve(luf, y, z); /* compute 1-norm of z = sum |z[i]| */ z_norm = 0.0; for (i = 1; i <= n; i++) z_norm += (z[i] >= 0.0 ? +z[i] : -z[i]); /* estimate 1-norm of inv(A) = (1-norm of z) / (1-norm of y) */ return z_norm / y_norm; } /* eof */