Mercurial > octave
view libinterp/dldfcn/ccolamd.cc @ 21691:263d18409fdf
Eliminate unused variable warnings for conditionally compiled code.
We had more or less decided not to bother trying to eliminate all
these warnings for cases in which external dependencies are missing.
But then we get people trying to fix these in various ways, so we
might as well do it for all cases and use a consistent method.
* oct-conf-post.in.h (octave_unused_parameter): New function for C++
code and new macro for C code.
* mk-octave-config-h.sh: Emit octave_unused_parameter function and
macro for octave-config.h.
* CSparse.cc, __delaunayn__.cc, __eigs__.cc, __fltk_uigetfile__.cc,
__glpk__.cc, __magick_read__.cc, __osmesa_print__.cc, __voronoi__.cc,
amd.cc, audiodevinfo.cc, audioread.cc, ccolamd.cc, cdisplay.c,
colamd.cc, convhulln.cc, dSparse.cc, dmperm.cc, fftw.cc, gl-render.cc,
lo-error.c, load-save.cc, ls-hdf5.cc, ls-mat5.cc, oct-hdf5-types.cc,
ov-base-int.cc, ov-bool-mat.cc, ov-bool-sparse.cc, ov-bool.cc,
ov-cell.cc, ov-class.cc, ov-complex.cc, ov-cx-mat.cc, ov-cx-sparse.cc,
ov-fcn-handle.cc, ov-fcn-inline.cc, ov-float.cc, ov-flt-complex.cc,
ov-flt-cx-mat.cc, ov-flt-re-mat.cc, ov-java.cc, ov-range.cc,
ov-re-mat.cc, ov-re-sparse.cc, ov-scalar.cc, ov-str-mat.cc,
ov-struct.cc, sparse-chol.cc, sparse-dmsolve.cc, sparse-lu.cc,
sparse-qr.cc, sparse-util.cc, symbfact.cc: Use octave_unused_parameter
to eliminate warnings for conditionally compiled code.
author | John W. Eaton <jwe@octave.org> |
---|---|
date | Fri, 13 May 2016 09:36:14 -0400 |
parents | d7a268e68e69 |
children | aba2e6293dd8 |
line wrap: on
line source
/* Copyright (C) 2005-2015 David Bateman This file is part of Octave. Octave 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. Octave 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 Octave; see the file COPYING. If not, see <http://www.gnu.org/licenses/>. */ // This is the octave interface to ccolamd, which bore the copyright given // in the help of the functions. #ifdef HAVE_CONFIG_H # include "config.h" #endif #include <cstdlib> #include <string> #include <vector> #include "ov.h" #include "defun-dld.h" #include "errwarn.h" #include "pager.h" #include "ov-re-mat.h" #include "ov-re-sparse.h" #include "ov-cx-sparse.h" #include "oct-sparse.h" #include "oct-locbuf.h" #if defined (OCTAVE_ENABLE_64) # define CCOLAMD_NAME(name) ccolamd_l ## name # define CSYMAMD_NAME(name) csymamd_l ## name #else # define CCOLAMD_NAME(name) ccolamd ## name # define CSYMAMD_NAME(name) csymamd ## name #endif DEFUN_DLD (ccolamd, args, nargout, "-*- texinfo -*-\n\ @deftypefn {} {@var{p} =} ccolamd (@var{S})\n\ @deftypefnx {} {@var{p} =} ccolamd (@var{S}, @var{knobs})\n\ @deftypefnx {} {@var{p} =} ccolamd (@var{S}, @var{knobs}, @var{cmember})\n\ @deftypefnx {} {[@var{p}, @var{stats}] =} ccolamd (@dots{})\n\ \n\ Constrained column approximate minimum degree permutation.\n\ \n\ @code{@var{p} = ccolamd (@var{S})} returns the column approximate minimum\n\ degree permutation vector for the sparse matrix @var{S}. For a\n\ non-symmetric matrix @var{S}, @code{@var{S}(:, @var{p})} tends to have\n\ sparser LU@tie{}factors than @var{S}.\n\ @code{chol (@var{S}(:, @var{p})' * @var{S}(:, @var{p}))} also tends to be\n\ sparser than @code{chol (@var{S}' * @var{S})}.\n\ @code{@var{p} = ccolamd (@var{S}, 1)} optimizes the ordering for\n\ @code{lu (@var{S}(:, @var{p}))}. The ordering is followed by a column\n\ elimination tree post-ordering.\n\ \n\ @var{knobs} is an optional 1-element to 5-element input vector, with a\n\ default value of @code{[0 10 10 1 0]} if not present or empty. Entries not\n\ present are set to their defaults.\n\ \n\ @table @code\n\ @item @var{knobs}(1)\n\ if nonzero, the ordering is optimized for @code{lu (S(:, p))}. It will be a\n\ poor ordering for @code{chol (@var{S}(:, @var{p})' * @var{S}(:, @var{p}))}.\n\ This is the most important knob for ccolamd.\n\ \n\ @item @var{knobs}(2)\n\ if @var{S} is m-by-n, rows with more than\n\ @code{max (16, @var{knobs}(2) * sqrt (n))} entries are ignored.\n\ \n\ @item @var{knobs}(3)\n\ columns with more than\n\ @code{max (16, @var{knobs}(3) * sqrt (min (@var{m}, @var{n})))} entries are\n\ ignored and ordered last in the output permutation\n\ (subject to the cmember constraints).\n\ \n\ @item @var{knobs}(4)\n\ if nonzero, aggressive absorption is performed.\n\ \n\ @item @var{knobs}(5)\n\ if nonzero, statistics and knobs are printed.\n\ \n\ @end table\n\ \n\ @var{cmember} is an optional vector of length @math{n}. It defines the\n\ constraints on the column ordering. If @code{@var{cmember}(j) = @var{c}},\n\ then column @var{j} is in constraint set @var{c} (@var{c} must be in the\n\ range 1 to n). In the output permutation @var{p}, all columns in set 1\n\ appear first, followed by all columns in set 2, and so on.\n\ @code{@var{cmember} = ones (1,n)} if not present or empty.\n\ @code{ccolamd (@var{S}, [], 1 : n)} returns @code{1 : n}\n\ \n\ @code{@var{p} = ccolamd (@var{S})} is about the same as\n\ @code{@var{p} = colamd (@var{S})}. @var{knobs} and its default values\n\ differ. @code{colamd} always does aggressive absorption, and it finds an\n\ ordering suitable for both @code{lu (@var{S}(:, @var{p}))} and @code{chol\n\ (@var{S}(:, @var{p})' * @var{S}(:, @var{p}))}; it cannot optimize its\n\ ordering for @code{lu (@var{S}(:, @var{p}))} to the extent that\n\ @code{ccolamd (@var{S}, 1)} can.\n\ \n\ @var{stats} is an optional 20-element output vector that provides data\n\ about the ordering and the validity of the input matrix @var{S}. Ordering\n\ statistics are in @code{@var{stats}(1 : 3)}. @code{@var{stats}(1)} and\n\ @code{@var{stats}(2)} are the number of dense or empty rows and columns\n\ ignored by @sc{ccolamd} and @code{@var{stats}(3)} is the number of garbage\n\ collections performed on the internal data structure used by @sc{ccolamd}\n\ (roughly of size @code{2.2 * nnz (@var{S}) + 4 * @var{m} + 7 * @var{n}}\n\ integers).\n\ \n\ @code{@var{stats}(4 : 7)} provide information if CCOLAMD was able to\n\ continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\ invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\ unsorted or contains duplicate entries, or zero if no such column exists.\n\ @code{@var{stats}(6)} is the last seen duplicate or out-of-order row\n\ index in the column index given by @code{@var{stats}(5)}, or zero if no\n\ such row index exists. @code{@var{stats}(7)} is the number of duplicate\n\ or out-of-order row indices. @code{@var{stats}(8 : 20)} is always zero in\n\ the current version of @sc{ccolamd} (reserved for future use).\n\ \n\ The authors of the code itself are @nospell{S. Larimore, T. Davis}\n\ (Univ. of Florida) and @nospell{S. Rajamanickam} in collaboration with\n\ @nospell{J. Bilbert and E. Ng}. Supported by the National Science Foundation\n\ @nospell{(DMS-9504974, DMS-9803599, CCR-0203270)}, and a grant from\n\ @nospell{Sandia} National Lab.\n\ See @url{http://www.cise.ufl.edu/research/sparse} for\n\ ccolamd, csymamd, amd, colamd, symamd, and other related orderings.\n\ @seealso{colamd, csymamd}\n\ @end deftypefn") { #ifdef HAVE_CCOLAMD int nargin = args.length (); if (nargin < 1 || nargin > 3) print_usage (); octave_value_list retval (nargout == 2 ? 2 : 1); int spumoni = 0; // Get knobs OCTAVE_LOCAL_BUFFER (double, knobs, CCOLAMD_KNOBS); CCOLAMD_NAME (_set_defaults) (knobs); // Check for user-passed knobs if (nargin > 1) { NDArray User_knobs = args(1).array_value (); int nel_User_knobs = User_knobs.numel (); if (nel_User_knobs > 0) knobs[CCOLAMD_LU] = (User_knobs(0) != 0); if (nel_User_knobs > 1) knobs[CCOLAMD_DENSE_ROW] = User_knobs(1); if (nel_User_knobs > 2) knobs[CCOLAMD_DENSE_COL] = User_knobs(2); if (nel_User_knobs > 3) knobs[CCOLAMD_AGGRESSIVE] = (User_knobs(3) != 0); if (nel_User_knobs > 4) spumoni = (User_knobs(4) != 0); // print knob settings if spumoni is set if (spumoni) { octave_stdout << "\nccolamd version " << CCOLAMD_MAIN_VERSION << "." << CCOLAMD_SUB_VERSION << ", " << CCOLAMD_DATE << ":\nknobs(1): " << User_knobs(0) << ", order for "; if (knobs[CCOLAMD_LU] != 0) octave_stdout << "lu (A)\n"; else octave_stdout << "chol (A'*A)\n"; if (knobs[CCOLAMD_DENSE_ROW] >= 0) octave_stdout << "knobs(2): " << User_knobs(1) << ", rows with > max (16," << knobs[CCOLAMD_DENSE_ROW] << "*sqrt (size(A,2)))" << " entries removed\n"; else octave_stdout << "knobs(2): " << User_knobs(1) << ", no dense rows removed\n"; if (knobs[CCOLAMD_DENSE_COL] >= 0) octave_stdout << "knobs(3): " << User_knobs(2) << ", cols with > max (16," << knobs[CCOLAMD_DENSE_COL] << "*sqrt (size(A)))" << " entries removed\n"; else octave_stdout << "knobs(3): " << User_knobs(2) << ", no dense columns removed\n"; if (knobs[CCOLAMD_AGGRESSIVE] != 0) octave_stdout << "knobs(4): " << User_knobs(3) << ", aggressive absorption: yes"; else octave_stdout << "knobs(4): " << User_knobs(3) << ", aggressive absorption: no"; octave_stdout << "knobs(5): " << User_knobs(4) << ", statistics and knobs printed\n"; } } octave_idx_type n_row, n_col, nnz; octave_idx_type *ridx, *cidx; SparseComplexMatrix scm; SparseMatrix sm; if (args(0).is_sparse_type ()) { if (args(0).is_complex_type ()) { scm = args(0). sparse_complex_matrix_value (); n_row = scm.rows (); n_col = scm.cols (); nnz = scm.nnz (); ridx = scm.xridx (); cidx = scm.xcidx (); } else { sm = args(0).sparse_matrix_value (); n_row = sm.rows (); n_col = sm.cols (); nnz = sm.nnz (); ridx = sm.xridx (); cidx = sm.xcidx (); } } else { if (args(0).is_complex_type ()) sm = SparseMatrix (real (args(0).complex_matrix_value ())); else sm = SparseMatrix (args(0).matrix_value ()); n_row = sm.rows (); n_col = sm.cols (); nnz = sm.nnz (); ridx = sm.xridx (); cidx = sm.xcidx (); } // Allocate workspace for ccolamd OCTAVE_LOCAL_BUFFER (octave_idx_type, p, n_col+1); for (octave_idx_type i = 0; i < n_col+1; i++) p[i] = cidx[i]; octave_idx_type Alen = CCOLAMD_NAME (_recommended) (nnz, n_row, n_col); OCTAVE_LOCAL_BUFFER (octave_idx_type, A, Alen); for (octave_idx_type i = 0; i < nnz; i++) A[i] = ridx[i]; OCTAVE_LOCAL_BUFFER (octave_idx_type, stats, CCOLAMD_STATS); if (nargin > 2) { NDArray in_cmember = args(2).array_value (); octave_idx_type cslen = in_cmember.numel (); OCTAVE_LOCAL_BUFFER (octave_idx_type, cmember, cslen); for (octave_idx_type i = 0; i < cslen; i++) // convert cmember from 1-based to 0-based cmember[i] = static_cast<octave_idx_type>(in_cmember(i) - 1); if (cslen != n_col) error ("ccolamd: CMEMBER must be of length equal to #cols of A"); // Order the columns (destroys A) if (! CCOLAMD_NAME () (n_row, n_col, Alen, A, p, knobs, stats, cmember)) { CCOLAMD_NAME (_report) (stats); error ("ccolamd: internal error!"); } } else { // Order the columns (destroys A) if (! CCOLAMD_NAME () (n_row, n_col, Alen, A, p, knobs, stats, 0)) { CCOLAMD_NAME (_report) (stats); error ("ccolamd: internal error!"); } } // return the permutation vector NDArray out_perm (dim_vector (1, n_col)); for (octave_idx_type i = 0; i < n_col; i++) out_perm(i) = p[i] + 1; retval(0) = out_perm; // print stats if spumoni > 0 if (spumoni > 0) CCOLAMD_NAME (_report) (stats); // Return the stats vector if (nargout == 2) { NDArray out_stats (dim_vector (1, CCOLAMD_STATS)); for (octave_idx_type i = 0 ; i < CCOLAMD_STATS ; i++) out_stats(i) = stats[i]; retval(1) = out_stats; // fix stats (5) and (6), for 1-based information on // jumbled matrix. note that this correction doesn't // occur if symamd returns FALSE out_stats(CCOLAMD_INFO1)++; out_stats(CCOLAMD_INFO2)++; } return retval; #else octave_unused_parameter (args); octave_unused_parameter (nargout); err_disabled_feature ("ccolamd", "CCOLAMD"); #endif } DEFUN_DLD (csymamd, args, nargout, "-*- texinfo -*-\n\ @deftypefn {} {@var{p} =} csymamd (@var{S})\n\ @deftypefnx {} {@var{p} =} csymamd (@var{S}, @var{knobs})\n\ @deftypefnx {} {@var{p} =} csymamd (@var{S}, @var{knobs}, @var{cmember})\n\ @deftypefnx {} {[@var{p}, @var{stats}] =} csymamd (@dots{})\n\ \n\ For a symmetric positive definite matrix @var{S}, return the permutation\n\ vector @var{p} such that @code{@var{S}(@var{p},@var{p})} tends to have a\n\ sparser Cholesky@tie{}factor than @var{S}.\n\ \n\ Sometimes @code{csymamd} works well for symmetric indefinite matrices too.\n\ The matrix @var{S} is assumed to be symmetric; only the strictly lower\n\ triangular part is referenced. @var{S} must be square. The ordering is\n\ followed by an elimination tree post-ordering.\n\ \n\ @var{knobs} is an optional 1-element to 3-element input vector, with a\n\ default value of @code{[10 1 0]}. Entries not present are set to their\n\ defaults.\n\ \n\ @table @code\n\ @item @var{knobs}(1)\n\ If @var{S} is n-by-n, then rows and columns with more than\n\ @code{max(16,@var{knobs}(1)*sqrt(n))} entries are ignored, and ordered\n\ last in the output permutation (subject to the cmember constraints).\n\ \n\ @item @var{knobs}(2)\n\ If nonzero, aggressive absorption is performed.\n\ \n\ @item @var{knobs}(3)\n\ If nonzero, statistics and knobs are printed.\n\ \n\ @end table\n\ \n\ @var{cmember} is an optional vector of length n. It defines the constraints\n\ on the ordering. If @code{@var{cmember}(j) = @var{S}}, then row/column j is\n\ in constraint set @var{c} (@var{c} must be in the range 1 to n). In the\n\ output permutation @var{p}, rows/columns in set 1 appear first, followed\n\ by all rows/columns in set 2, and so on. @code{@var{cmember} = ones (1,n)}\n\ if not present or empty. @code{csymamd (@var{S},[],1:n)} returns\n\ @code{1:n}.\n\ \n\ @code{@var{p} = csymamd (@var{S})} is about the same as\n\ @code{@var{p} = symamd (@var{S})}. @var{knobs} and its default values\n\ differ.\n\ \n\ @code{@var{stats}(4:7)} provide information if CCOLAMD was able to\n\ continue. The matrix is OK if @code{@var{stats}(4)} is zero, or 1 if\n\ invalid. @code{@var{stats}(5)} is the rightmost column index that is\n\ unsorted or contains duplicate entries, or zero if no such column exists.\n\ @code{@var{stats}(6)} is the last seen duplicate or out-of-order row\n\ index in the column index given by @code{@var{stats}(5)}, or zero if no\n\ such row index exists. @code{@var{stats}(7)} is the number of duplicate\n\ or out-of-order row indices. @code{@var{stats}(8:20)} is always zero in\n\ the current version of @sc{ccolamd} (reserved for future use).\n\ \n\ The authors of the code itself are @nospell{S. Larimore, T. Davis}\n\ (Univ. of Florida) and @nospell{S. Rajamanickam} in collaboration with\n\ @nospell{J. Bilbert and E. Ng}. Supported by the National Science Foundation\n\ @nospell{(DMS-9504974, DMS-9803599, CCR-0203270)}, and a grant from\n\ @nospell{Sandia} National Lab.\n\ See @url{http://www.cise.ufl.edu/research/sparse} for\n\ ccolamd, csymamd, amd, colamd, symamd, and other related orderings.\n\ @seealso{symamd, ccolamd}\n\ @end deftypefn") { #if defined (HAVE_CCOLAMD) int nargin = args.length (); if (nargin < 1 || nargin > 3) print_usage (); octave_value_list retval (nargout == 2 ? 2 : 1); int spumoni = 0; // Get knobs OCTAVE_LOCAL_BUFFER (double, knobs, CCOLAMD_KNOBS); CCOLAMD_NAME (_set_defaults) (knobs); // Check for user-passed knobs if (nargin > 1) { NDArray User_knobs = args(1).array_value (); int nel_User_knobs = User_knobs.numel (); if (nel_User_knobs > 0) knobs[CCOLAMD_DENSE_ROW] = User_knobs(0); if (nel_User_knobs > 0) knobs[CCOLAMD_AGGRESSIVE] = User_knobs(1); if (nel_User_knobs > 1) spumoni = static_cast<int> (User_knobs(2)); // print knob settings if spumoni is set if (spumoni) { octave_stdout << "\ncsymamd version " << CCOLAMD_MAIN_VERSION << "." << CCOLAMD_SUB_VERSION << ", " << CCOLAMD_DATE << "\n"; if (knobs[CCOLAMD_DENSE_ROW] >= 0) octave_stdout << "knobs(1): " << User_knobs(0) << ", rows/cols with > max (16," << knobs[CCOLAMD_DENSE_ROW] << "*sqrt (size(A,2)))" << " entries removed\n"; else octave_stdout << "knobs(1): " << User_knobs(0) << ", no dense rows/cols removed\n"; if (knobs[CCOLAMD_AGGRESSIVE] != 0) octave_stdout << "knobs(2): " << User_knobs(1) << ", aggressive absorption: yes"; else octave_stdout << "knobs(2): " << User_knobs(1) << ", aggressive absorption: no"; octave_stdout << "knobs(3): " << User_knobs(2) << ", statistics and knobs printed\n"; } } octave_idx_type n_row, n_col; octave_idx_type *ridx, *cidx; SparseMatrix sm; SparseComplexMatrix scm; if (args(0).is_sparse_type ()) { if (args(0).is_complex_type ()) { scm = args(0).sparse_complex_matrix_value (); n_row = scm.rows (); n_col = scm.cols (); ridx = scm.xridx (); cidx = scm.xcidx (); } else { sm = args(0).sparse_matrix_value (); n_row = sm.rows (); n_col = sm.cols (); ridx = sm.xridx (); cidx = sm.xcidx (); } } else { if (args(0).is_complex_type ()) sm = SparseMatrix (real (args(0).complex_matrix_value ())); else sm = SparseMatrix (args(0).matrix_value ()); n_row = sm.rows (); n_col = sm.cols (); ridx = sm.xridx (); cidx = sm.xcidx (); } if (n_row != n_col) err_square_matrix_required ("csymamd", "S"); // Allocate workspace for symamd OCTAVE_LOCAL_BUFFER (octave_idx_type, perm, n_col+1); OCTAVE_LOCAL_BUFFER (octave_idx_type, stats, CCOLAMD_STATS); if (nargin > 2) { NDArray in_cmember = args(2).array_value (); octave_idx_type cslen = in_cmember.numel (); OCTAVE_LOCAL_BUFFER (octave_idx_type, cmember, cslen); for (octave_idx_type i = 0; i < cslen; i++) // convert cmember from 1-based to 0-based cmember[i] = static_cast<octave_idx_type>(in_cmember(i) - 1); if (cslen != n_col) error ("csymamd: CMEMBER must be of length equal to #cols of A"); if (! CSYMAMD_NAME () (n_col, ridx, cidx, perm, knobs, stats, &calloc, &free, cmember, -1)) { CSYMAMD_NAME (_report)(stats); error ("csymamd: internal error!"); } } else { if (! CSYMAMD_NAME () (n_col, ridx, cidx, perm, knobs, stats, &calloc, &free, 0, -1)) { CSYMAMD_NAME (_report)(stats); error ("csymamd: internal error!"); } } // return the permutation vector NDArray out_perm (dim_vector (1, n_col)); for (octave_idx_type i = 0; i < n_col; i++) out_perm(i) = perm[i] + 1; retval(0) = out_perm; // print stats if spumoni > 0 if (spumoni > 0) CSYMAMD_NAME (_report)(stats); // Return the stats vector if (nargout == 2) { NDArray out_stats (dim_vector (1, CCOLAMD_STATS)); for (octave_idx_type i = 0 ; i < CCOLAMD_STATS ; i++) out_stats(i) = stats[i]; retval(1) = out_stats; // fix stats (5) and (6), for 1-based information on // jumbled matrix. note that this correction doesn't // occur if symamd returns FALSE out_stats(CCOLAMD_INFO1)++; out_stats(CCOLAMD_INFO2)++; } return retval; #else octave_unused_parameter (args); octave_unused_parameter (nargout); err_disabled_feature ("csymamd", "CCOLAMD"); #endif }