08-Jan-2022 10:05:45 st_to_ccs_test(): MATLAB/Octave version 9.8.0.1380330 (R2020a) Update 2 Test st_to_ccs(). st_to_ccs_test01 Convert a sparse matrix from ST to CCS format. ST: sparse triplet, I, J, A. CCs: compressed column, I, CC, A. Sparse Triplet (ST) header: Minimum row index I_MIN = 1 Maximum row index I_MAX = 5 Minimum col index J_MIN = 1 Maximum col index J_MAX = 5 Number of rows M = 5 Number of columns N = 5 Number of nonzeros NST = 12 The matrix in ST format: 1 1 1 2.00000000 2 1 2 3.00000000 3 2 1 3.00000000 4 2 3 4.00000000 5 2 5 6.00000000 6 3 2 -1.00000000 7 3 3 -3.00000000 8 3 4 2.00000000 9 4 3 1.00000000 10 5 2 4.00000000 11 5 3 2.00000000 12 5 5 1.00000000 Number of CCS values = 12 CCS Matrix: # I J A ---- ---- ---- -------------- 1 1 1 2 2 2 1 3 3 1 2 3 4 3 2 -1 5 5 2 4 6 2 3 4 7 3 3 -3 8 4 3 1 9 5 3 2 10 3 4 2 11 2 5 6 12 5 5 1 st_to_ccs_test02 Convert a sparse matrix from ST to CC format. ST: sparse triplet, I, J, A. CCS: compressed column, I, CC, A. This matrix is read from the file "west_st.txt" Sparse Triplet (ST) header: Minimum row index I_MIN = 1 Maximum row index I_MAX = 67 Minimum col index J_MIN = 1 Maximum col index J_MAX = 67 Number of rows M = 67 Number of columns N = 67 Number of nonzeros NST = 299 The matrix in ST format: 1 45 56 -1.86335400 2 55 62 -1.86335400 3 30 38 -1.56739800 4 45 57 -1.49068300 5 55 63 -1.49068300 6 10 13 -1.26582300 7 30 39 -1.25391800 8 45 58 -1.11801200 9 55 64 -1.11801200 10 16 32 -1.05000000 11 17 33 -1.05000000 12 18 34 -1.05000000 13 19 35 -1.05000000 14 20 36 -1.05000000 15 25 32 -1.05000000 16 26 33 -1.05000000 17 27 34 -1.05000000 18 28 35 -1.05000000 19 29 36 -1.05000000 20 10 14 -1.01265800 21 11 21 -1.00000000 22 12 22 -1.00000000 23 13 23 -1.00000000 24 14 24 -1.00000000 25 15 25 -1.00000000 26 31 50 -0.97222220 27 32 51 -0.97222220 28 33 52 -0.97222220 29 34 53 -0.97222220 30 35 54 -0.97222220 31 40 50 -0.97222220 32 41 51 -0.97222220 33 42 52 -0.97222220 34 43 53 -0.97222220 35 44 54 -0.97222220 36 36 26 -0.95831870 37 37 27 -0.95831870 38 38 28 -0.95831870 39 39 29 -0.95831870 40 50 56 -0.94444440 41 51 57 -0.94444440 42 52 58 -0.94444440 43 53 59 -0.94444440 44 54 60 -0.94444440 45 30 40 -0.94043890 46 21 2 -0.91595330 47 22 3 -0.91595330 48 23 4 -0.91595330 49 24 5 -0.91595330 50 1 8 -0.83418180 51 2 9 -0.83418180 52 3 10 -0.83418180 53 4 11 -0.83418180 54 46 44 -0.82422480 55 47 45 -0.82422480 56 48 46 -0.82422480 57 49 47 -0.82422480 58 5 2 -0.80000000 59 6 3 -0.80000000 60 7 4 -0.80000000 61 8 5 -0.80000000 62 9 6 -0.80000000 63 10 15 -0.75949370 64 45 59 -0.74534160 65 55 65 -0.74534160 66 30 41 -0.62695920 67 10 16 -0.50632910 68 45 60 -0.37267080 69 55 66 -0.37267080 70 1 18 -0.33615560 71 30 42 -0.31347960 72 2 18 -0.29391960 73 21 43 -0.27884160 74 5 1 -0.27884160 75 22 43 -0.26801860 76 6 1 -0.26801860 77 53 55 -0.26677570 78 54 55 -0.26307060 79 49 67 -0.25411930 80 10 17 -0.25316460 81 38 61 -0.24756750 82 48 67 -0.24214980 83 34 49 -0.23628450 84 43 49 -0.23628450 85 37 61 -0.23564690 86 23 43 -0.23237170 87 7 1 -0.23237170 88 33 49 -0.23039170 89 42 49 -0.23039170 90 18 31 -0.22862640 91 27 31 -0.22862640 92 17 31 -0.22329970 93 26 31 -0.22329970 94 3 18 -0.22148150 95 13 20 -0.21442060 96 12 20 -0.21403920 97 52 55 -0.21220560 98 39 61 -0.20748730 99 11 20 -0.20717590 100 16 31 -0.20709860 101 25 31 -0.20709860 102 36 61 -0.20699540 103 19 31 -0.20245280 104 28 31 -0.20245280 105 14 20 -0.19867680 106 32 49 -0.19477110 107 41 49 -0.19477110 108 47 67 -0.19185570 109 35 49 -0.18039000 110 44 49 -0.18039000 111 15 20 -0.16568740 112 31 49 -0.15816260 113 40 49 -0.15816260 114 24 43 -0.15750820 115 8 1 -0.15750820 116 51 55 -0.15149080 117 46 67 -0.14433540 118 20 31 -0.13852260 119 29 31 -0.13852260 120 4 18 -0.11898600 121 50 55 -0.10645730 122 9 1 -0.06325978 123 9 7 0.01178291 124 29 1 0.03162989 125 8 7 0.04759439 126 40 55 0.05322864 127 41 55 0.07574542 128 28 1 0.07875411 129 25 37 0.08147449 130 7 7 0.08859262 131 31 37 0.09052721 132 29 37 0.09241909 133 26 37 0.09789015 134 20 20 0.09941246 135 35 37 0.10268790 136 42 55 0.10610280 137 32 37 0.10876680 138 27 37 0.11316080 139 28 37 0.11505550 140 27 1 0.11618590 141 6 7 0.11756790 142 19 20 0.11920610 143 16 20 0.12430550 144 33 37 0.12573420 145 34 37 0.12783940 146 17 20 0.12842350 147 18 20 0.12865240 148 44 55 0.13153530 149 43 55 0.13338780 150 26 1 0.13400930 151 5 7 0.13446220 152 25 1 0.13942080 153 31 44 0.25000000 154 32 45 0.25000000 155 33 46 0.25000000 156 34 47 0.25000000 157 35 48 0.25000000 158 11 13 0.33333330 159 12 14 0.33333330 160 13 15 0.33333330 161 14 16 0.33333330 162 15 17 0.33333330 163 25 2 0.40000000 164 26 3 0.40000000 165 27 4 0.40000000 166 28 5 0.40000000 167 29 6 0.40000000 168 5 13 0.40000000 169 5 8 0.40000000 170 6 14 0.40000000 171 6 9 0.40000000 172 7 15 0.40000000 173 7 10 0.40000000 174 8 11 0.40000000 175 8 16 0.40000000 176 9 12 0.40000000 177 9 17 0.40000000 178 50 62 0.44444440 179 51 63 0.44444440 180 52 64 0.44444440 181 53 65 0.44444440 182 54 66 0.44444440 183 16 26 0.45000000 184 17 27 0.45000000 185 18 28 0.45000000 186 19 29 0.45000000 187 20 30 0.45000000 188 40 56 0.47222220 189 41 57 0.47222220 190 42 58 0.47222220 191 43 59 0.47222220 192 44 60 0.47222220 193 40 26 0.50000000 194 41 27 0.50000000 195 42 28 0.50000000 196 43 29 0.50000000 197 44 30 0.50000000 198 50 44 0.50000000 199 51 45 0.50000000 200 52 46 0.50000000 201 53 47 0.50000000 202 54 48 0.50000000 203 4 16 0.50632910 204 16 21 0.60000000 205 17 22 0.60000000 206 18 23 0.60000000 207 19 24 0.60000000 208 20 25 0.60000000 209 24 41 0.62695920 210 25 38 0.65000000 211 26 39 0.65000000 212 27 40 0.65000000 213 28 41 0.65000000 214 29 42 0.65000000 215 15 19 0.66666670 216 31 38 0.72222220 217 32 39 0.72222220 218 33 40 0.72222220 219 34 41 0.72222220 220 35 42 0.72222220 221 39 59 0.74534160 222 49 65 0.74534160 223 3 15 0.75949370 224 23 40 0.94043890 225 60 32 0.50000000 226 60 33 0.50000000 227 60 34 0.50000000 228 60 35 0.50000000 229 60 36 0.50000000 230 30 43 1.00000000 231 45 61 1.00000000 232 55 67 1.00000000 233 56 19 1.00000000 234 57 11 1.00000000 235 57 12 1.00000000 236 57 8 1.00000000 237 57 9 1.00000000 238 57 10 1.00000000 239 58 13 1.00000000 240 58 14 1.00000000 241 58 15 1.00000000 242 58 16 1.00000000 243 58 17 1.00000000 244 59 21 1.00000000 245 59 22 1.00000000 246 59 23 1.00000000 247 59 24 1.00000000 248 59 25 1.00000000 249 60 32 0.50000000 250 60 33 0.50000000 251 60 34 0.50000000 252 60 35 0.50000000 253 60 36 0.50000000 254 61 2 1.00000000 255 61 3 1.00000000 256 61 4 1.00000000 257 61 5 1.00000000 258 61 6 1.00000000 259 62 38 1.00000000 260 62 39 1.00000000 261 62 40 1.00000000 262 62 41 1.00000000 263 62 42 1.00000000 264 63 50 1.00000000 265 63 51 1.00000000 266 63 52 1.00000000 267 63 53 1.00000000 268 63 54 1.00000000 269 64 26 1.00000000 270 64 27 1.00000000 271 64 28 1.00000000 272 64 29 1.00000000 273 64 30 1.00000000 274 65 56 1.00000000 275 65 57 1.00000000 276 65 58 1.00000000 277 65 59 1.00000000 278 65 60 1.00000000 279 66 44 1.00000000 280 66 45 1.00000000 281 66 46 1.00000000 282 66 47 1.00000000 283 66 48 1.00000000 284 67 62 1.00000000 285 67 63 1.00000000 286 67 64 1.00000000 287 67 65 1.00000000 288 67 66 1.00000000 289 10 18 1.00000000 290 2 14 1.01265800 291 38 58 1.11801200 292 48 64 1.11801200 293 22 39 1.25391800 294 1 13 1.26582300 295 37 57 1.49068300 296 47 63 1.49068300 297 21 38 1.56739800 298 36 56 1.86335400 299 46 62 1.86335400 Number of CCS values = 294 CCS Matrix: # I J A ---- ---- ---- -------------- 1 5 1 -0.2788416 2 6 1 -0.2680186 3 7 1 -0.2323717 4 8 1 -0.1575082 5 9 1 -0.06325978 6 25 1 0.1394208 7 26 1 0.1340093 8 27 1 0.1161859 9 28 1 0.078754112 10 29 1 0.03162989 11 5 2 -0.8 12 21 2 -0.9159533 13 25 2 0.4 14 61 2 1 15 6 3 -0.8 16 22 3 -0.9159533 17 26 3 0.4 18 61 3 1 19 7 4 -0.8 20 23 4 -0.9159533 21 27 4 0.4 22 61 4 1 23 8 5 -0.8 24 24 5 -0.9159533 25 28 5 0.4 26 61 5 1 27 9 6 -0.8 28 29 6 0.4 29 61 6 1 30 5 7 0.1344622 31 6 7 0.1175679 32 7 7 0.088592619 33 8 7 0.047594391 34 9 7 0.01178291 35 1 8 -0.8341818 36 5 8 0.4 37 57 8 1 38 2 9 -0.8341818 39 6 9 0.4 40 57 9 1 41 3 10 -0.8341818 42 7 10 0.4 43 57 10 1 44 4 11 -0.8341818 45 8 11 0.4 46 57 11 1 47 9 12 0.4 48 57 12 1 49 1 13 1.265823 50 5 13 0.4 51 10 13 -1.265823 52 11 13 0.3333333 53 58 13 1 54 2 14 1.012658 55 6 14 0.4 56 10 14 -1.012658 57 12 14 0.3333333 58 58 14 1 59 3 15 0.7594937 60 7 15 0.4 61 10 15 -0.7594937 62 13 15 0.3333333 63 58 15 1 64 4 16 0.5063291 65 8 16 0.4 66 10 16 -0.5063291 67 14 16 0.3333333 68 58 16 1 69 9 17 0.4 70 10 17 -0.2531646 71 15 17 0.3333333 72 58 17 1 73 1 18 -0.3361556 74 2 18 -0.2939196 75 3 18 -0.2214815 76 4 18 -0.118986 77 10 18 1 78 15 19 0.6666667 79 56 19 1 80 11 20 -0.2071759 81 12 20 -0.2140392 82 13 20 -0.2144206 83 14 20 -0.1986768 84 15 20 -0.1656874 85 16 20 0.1243055 86 17 20 0.1284235 87 18 20 0.1286524 88 19 20 0.1192061 89 20 20 0.099412464 90 11 21 -1 91 16 21 0.6 92 59 21 1 93 12 22 -1 94 17 22 0.6 95 59 22 1 96 13 23 -1 97 18 23 0.6 98 59 23 1 99 14 24 -1 100 19 24 0.6 101 59 24 1 102 15 25 -1 103 20 25 0.6 104 59 25 1 105 16 26 0.45 106 36 26 -0.9583187 107 40 26 0.5 108 64 26 1 109 17 27 0.45 110 37 27 -0.9583187 111 41 27 0.5 112 64 27 1 113 18 28 0.45 114 38 28 -0.9583187 115 42 28 0.5 116 64 28 1 117 19 29 0.45 118 39 29 -0.9583187 119 43 29 0.5 120 64 29 1 121 20 30 0.45 122 44 30 0.5 123 64 30 1 124 16 31 -0.2070986 125 17 31 -0.2232997 126 18 31 -0.2286264 127 19 31 -0.2024528 128 20 31 -0.1385226 129 25 31 -0.2070986 130 26 31 -0.2232997 131 27 31 -0.2286264 132 28 31 -0.2024528 133 29 31 -0.1385226 134 16 32 -1.05 135 25 32 -1.05 136 60 32 1 137 17 33 -1.05 138 26 33 -1.05 139 60 33 1 140 18 34 -1.05 141 27 34 -1.05 142 60 34 1 143 19 35 -1.05 144 28 35 -1.05 145 60 35 1 146 20 36 -1.05 147 29 36 -1.05 148 60 36 1 149 25 37 0.08147449 150 26 37 0.097890154 151 27 37 0.1131608 152 28 37 0.1150555 153 29 37 0.092419088 154 31 37 0.090527207 155 32 37 0.1087668 156 33 37 0.1257342 157 34 37 0.1278394 158 35 37 0.1026879 159 21 38 1.567398 160 25 38 0.65 161 30 38 -1.567398 162 31 38 0.7222222 163 62 38 1 164 22 39 1.253918 165 26 39 0.65 166 30 39 -1.253918 167 32 39 0.7222222 168 62 39 1 169 23 40 0.9404389 170 27 40 0.65 171 30 40 -0.9404389 172 33 40 0.7222222 173 62 40 1 174 24 41 0.6269592 175 28 41 0.65 176 30 41 -0.6269592 177 34 41 0.7222222 178 62 41 1 179 29 42 0.65 180 30 42 -0.3134796 181 35 42 0.7222222 182 62 42 1 183 21 43 -0.2788416 184 22 43 -0.2680186 185 23 43 -0.2323717 186 24 43 -0.1575082 187 30 43 1 188 31 44 0.25 189 46 44 -0.8242248 190 50 44 0.5 191 66 44 1 192 32 45 0.25 193 47 45 -0.8242248 194 51 45 0.5 195 66 45 1 196 33 46 0.25 197 48 46 -0.8242248 198 52 46 0.5 199 66 46 1 200 34 47 0.25 201 49 47 -0.8242248 202 53 47 0.5 203 66 47 1 204 35 48 0.25 205 54 48 0.5 206 66 48 1 207 31 49 -0.1581626 208 32 49 -0.1947711 209 33 49 -0.2303917 210 34 49 -0.2362845 211 35 49 -0.18039 212 40 49 -0.1581626 213 41 49 -0.1947711 214 42 49 -0.2303917 215 43 49 -0.2362845 216 44 49 -0.18039 217 31 50 -0.9722222 218 40 50 -0.9722222 219 63 50 1 220 32 51 -0.9722222 221 41 51 -0.9722222 222 63 51 1 223 33 52 -0.9722222 224 42 52 -0.9722222 225 63 52 1 226 34 53 -0.9722222 227 43 53 -0.9722222 228 63 53 1 229 35 54 -0.9722222 230 44 54 -0.9722222 231 63 54 1 232 40 55 0.053228639 233 41 55 0.075745419 234 42 55 0.1061028 235 43 55 0.1333878 236 44 55 0.1315353 237 50 55 -0.1064573 238 51 55 -0.1514908 239 52 55 -0.2122056 240 53 55 -0.2667757 241 54 55 -0.2630706 242 36 56 1.863354 243 40 56 0.4722222 244 45 56 -1.863354 245 50 56 -0.9444444 246 65 56 1 247 37 57 1.490683 248 41 57 0.4722222 249 45 57 -1.490683 250 51 57 -0.9444444 251 65 57 1 252 38 58 1.118012 253 42 58 0.4722222 254 45 58 -1.118012 255 52 58 -0.9444444 256 65 58 1 257 39 59 0.7453416 258 43 59 0.4722222 259 45 59 -0.7453416 260 53 59 -0.9444444 261 65 59 1 262 44 60 0.4722222 263 45 60 -0.3726708 264 54 60 -0.9444444 265 65 60 1 266 36 61 -0.2069954 267 37 61 -0.2356469 268 38 61 -0.2475675 269 39 61 -0.2074873 270 45 61 1 271 46 62 1.863354 272 50 62 0.4444444 273 55 62 -1.863354 274 67 62 1 275 47 63 1.490683 276 51 63 0.4444444 277 55 63 -1.490683 278 67 63 1 279 48 64 1.118012 280 52 64 0.4444444 281 55 64 -1.118012 282 67 64 1 283 49 65 0.7453416 284 53 65 0.4444444 285 55 65 -0.7453416 286 67 65 1 287 54 66 0.4444444 288 55 66 -0.3726708 289 67 66 1 290 46 67 -0.1443354 291 47 67 -0.1918557 292 48 67 -0.2421498 293 49 67 -0.2541193 294 55 67 1 st_to_ccs_test03 Convert a sparse matrix from ST to CCS format. ST: sparse triplet, I, J, A. CCS: compressed column, I, CC, A. The ST matrix is read from the file "west_st.txt" and the CCS matrix is written to the files: "west_icc.txt", "west_ccc.txt", and "west_acc.txt". Sparse Triplet (ST) header: Minimum row index I_MIN = 1 Maximum row index I_MAX = 67 Minimum col index J_MIN = 1 Maximum col index J_MAX = 67 Number of rows M = 67 Number of columns N = 67 Number of nonzeros NST = 299 Number of CCS values = 294 ST_TO_CCS_TEST04(): Convert a sparse matrix from ST to CCS format. ST: sparse triplet, I, J, A. CCS: compressed column, I, CC, A. The ST matrix is the Wathen finite element matrix. It has many repeated index pairs. To check, compare ACC*X - AST*X for a random X. Number of ST values = 576 Number of rows and columns N = 40 NX = 3 NY = 3 NST = 576 Sparse Triplet (ST) header: Minimum row index I_MIN = 1 Maximum row index I_MAX = 40 Minimum col index J_MIN = 1 Maximum col index J_MAX = 40 Number of rows M = 40 Number of columns N = 40 Number of nonzeros NST = 576 Number of CCS values = 472 || ACC*X - AST*X|| = 1.70341e-12 st_to_ccs_test(): Normal end of execution. 08-Jan-2022 10:05:46