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comparison liboctave/COLAMD/colamdmex.c @ 5438:49ff3dd744ee

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[project @ 2005-09-04 12:25:21 by dbateman]
author dbateman
date Sun, 04 Sep 2005 12:25:21 +0000
parents 57077d0ddc8e
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5437:009606303874 5438:49ff3dd744ee
1 /* ========================================================================== */ 1 /* ========================================================================== */
2 /* === colamd mexFunction =================================================== */ 2 /* === colamd mexFunction =================================================== */
3 /* ========================================================================== */ 3 /* ========================================================================== */
4 4
5 /* 5 /* COLAMD Version 2.4
6
6 Usage: 7 Usage:
7 8
8 P = colamd (A) ; 9 P = colamd (A) ;
9
10 P = colamd (A, knobs) ;
11
12 [ P, stats ] = colamd (A) ;
13
14 [ P, stats ] = colamd (A, knobs) ; 10 [ P, stats ] = colamd (A, knobs) ;
15 11
16 Returns a permutation vector P such that the LU factorization of A (:,P) 12 see colamd.m for a description.
17 tends to be sparser than that of A. The Cholesky factorization of
18 (A (:,P))'*(A (:,P)) will also tend to be sparser than that of A'*A.
19 This routine provides the same functionality as COLMMD, but is much faster
20 and returns a better permutation vector. Note that the COLMMD m-file in
21 MATLAB 5.2 also performs a column elimination tree post-ordering. This
22 mexFunction does not do this post-ordering. This mexFunction is a
23 replacement for the p = sparsfun ('colmmd', A) operation.
24
25 The knobs and stats vectors are optional:
26
27 knobs (1) rows with more than (knobs (1))*n_col entries
28 are removed prior to ordering. If knobs is not present,
29 then the default is used (0.5).
30
31 knobs (2) columns with more than (knobs (2))*n_row entries
32 are removed prior to ordering, and placed last in the
33 column permutation. If knobs is not present,
34 then the default is used (0.5).
35
36 knobs (3) print level, similar to spparms ('spumoni')
37
38 stats (1) the number of dense (or empty) rows ignored
39
40 stats (2) the number of dense (or empty) columms. These
41 are ordered last, in their natural order.
42
43 stats (3) the number of garbage collections performed.
44
45 stats (4) return status:
46
47 0: matrix is a valid MATLAB matrix.
48
49 1: matrix has duplicate entries or unsorted columns.
50 This should not occur in a valid MATLAB matrix,
51 but the ordering proceeded anyway by sorting the
52 row indices in each column and by ignoring the
53 duplicate row indices in each column. See
54 stats (5:7) for more information.
55
56 stats (5) highest numbered column that is unsorted or has
57 duplicate entries (zero if none)
58
59 stats (6) last seen duplicate or unsorted row index
60 (zero if none)
61
62 stats (7) number of duplicate or unsorted row indices
63 13
64 Authors: 14 Authors:
65 15
66 The authors of the code itself are Stefan I. Larimore and Timothy A. 16 The authors of the code itself are Stefan I. Larimore and Timothy A.
67 Davis (davis@cise.ufl.edu), University of Florida. The algorithm was 17 Davis (davis at cise.ufl.edu), University of Florida. The algorithm was
68 developed in collaboration with John Gilbert, Xerox PARC, and Esmond 18 developed in collaboration with John Gilbert, Xerox PARC, and Esmond
69 Ng, Oak Ridge National Laboratory. 19 Ng, Oak Ridge National Laboratory.
70 20
71 Date:
72
73 September 8, 2003. Version 2.3.
74
75 Acknowledgements: 21 Acknowledgements:
76 22
77 This work was supported by the National Science Foundation, under 23 This work was supported by the National Science Foundation, under
78 grants DMS-9504974 and DMS-9803599. 24 grants DMS-9504974 and DMS-9803599.
79 25
80 Notice: 26 Notice:
81 27
82 Copyright (c) 1998-2003 by the University of Florida. 28 Copyright (c) 1998-2005, Timothy A. Davis, All Rights Reserved.
83 All Rights Reserved.
84 29
85 See http://www.cise.ufl.edu/research/sparse/colamd (the colamd.c 30 See http://www.cise.ufl.edu/research/sparse/colamd (the colamd.c
86 file) for the License. 31 file) for the License.
87 32
88 Availability: 33 Availability:
136 int i ; /* loop counter */ 81 int i ; /* loop counter */
137 mxArray *Ainput ; /* input matrix handle */ 82 mxArray *Ainput ; /* input matrix handle */
138 int spumoni ; /* verbosity variable */ 83 int spumoni ; /* verbosity variable */
139 int stats [COLAMD_STATS] ; /* stats for colamd */ 84 int stats [COLAMD_STATS] ; /* stats for colamd */
140 85
86 colamd_printf = mexPrintf ; /* COLAMD printf routine */
87
141 /* === Check inputs ===================================================== */ 88 /* === Check inputs ===================================================== */
142 89
143 if (nrhs < 1 || nrhs > 2 || nlhs < 0 || nlhs > 2) 90 if (nrhs < 1 || nrhs > 2 || nlhs < 0 || nlhs > 2)
144 { 91 {
145 mexErrMsgTxt ( 92 mexErrMsgTxt (
154 /* check for user-passed knobs */ 101 /* check for user-passed knobs */
155 if (nrhs == 2) 102 if (nrhs == 2)
156 { 103 {
157 in_knobs = mxGetPr (prhs [1]) ; 104 in_knobs = mxGetPr (prhs [1]) ;
158 i = mxGetNumberOfElements (prhs [1]) ; 105 i = mxGetNumberOfElements (prhs [1]) ;
159 if (i > 0) knobs [COLAMD_DENSE_ROW] = in_knobs [COLAMD_DENSE_ROW] ; 106 if (i > 0) knobs [COLAMD_DENSE_ROW] = in_knobs [0] ;
160 if (i > 1) knobs [COLAMD_DENSE_COL] = in_knobs [COLAMD_DENSE_COL] ; 107 if (i > 1) knobs [COLAMD_DENSE_COL] = in_knobs [1] ;
161 if (i > 2) spumoni = (int) in_knobs [2] ; 108 if (i > 2) spumoni = (int) (in_knobs [2] != 0) ;
162 } 109 }
163 110
164 /* print knob settings if spumoni is set */ 111 /* print knob settings if spumoni is set */
165 if (spumoni > 0) 112 if (spumoni)
166 { 113 {
167 mexPrintf ("colamd: dense row fraction: %f\n", 114 mexPrintf ("\ncolamd version %d.%d, %s:\n",
168 knobs [COLAMD_DENSE_ROW]) ; 115 COLAMD_MAIN_VERSION, COLAMD_SUB_VERSION, COLAMD_DATE) ;
169 mexPrintf ("colamd: dense col fraction: %f\n", 116 if (knobs [COLAMD_DENSE_ROW] >= 0)
170 knobs [COLAMD_DENSE_COL]) ; 117 {
118 mexPrintf ("knobs(1): %g, rows with > max(16,%g*sqrt(size(A,2)))"
119 " entries removed\n", in_knobs [0], knobs [COLAMD_DENSE_ROW]) ;
120 }
121 else
122 {
123 mexPrintf ("knobs(1): %g, only completely dense rows removed\n",
124 in_knobs [0]) ;
125 }
126 if (knobs [COLAMD_DENSE_COL] >= 0)
127 {
128 mexPrintf ("knobs(2): %g, cols with > max(16,%g*sqrt(min(size(A)))"
129 " entries removed\n", in_knobs [1], knobs [COLAMD_DENSE_COL]) ;
130 }
131 else
132 {
133 mexPrintf ("knobs(2): %g, only completely dense columns removed\n",
134 in_knobs [1]) ;
135 }
136 mexPrintf ("knobs(3): %g, statistics and knobs printed\n",
137 in_knobs [2]) ;
171 } 138 }
172 139
173 /* === If A is full, convert to a sparse matrix ========================= */ 140 /* === If A is full, convert to a sparse matrix ========================= */
174 141
175 Ainput = (mxArray *) prhs [0] ; 142 Ainput = (mxArray *) prhs [0] ;
225 } 192 }
226 mxFree (p) ; 193 mxFree (p) ;
227 194
228 /* === Return the stats vector ========================================== */ 195 /* === Return the stats vector ========================================== */
229 196
230 /* print stats if spumoni > 0 */ 197 /* print stats if spumoni is set */
231 if (spumoni > 0) 198 if (spumoni)
232 { 199 {
233 colamd_report (stats) ; 200 colamd_report (stats) ;
234 } 201 }
235 202
236 if (nlhs == 2) 203 if (nlhs == 2)