Research Article
Global Convergence of a Nonlinear Conjugate Gradient Method
Table 3
The numerical results of the CG-DESCENT method.
| Problem | Dim | NI | NF | NG | CPU |
| ROSE | 2 | 36 | 132 | 107 | 0.2371 | FROTH | 2 | 12 | 64 | 48 | 0.0462 | BADSCP | 2 | 40 | 213 | 189 | 0.2000 | BADSCB | 2 | 16 | 101 | 88 | 0.1212 | BEALE | 2 | 11 | 45 | 33 | 0.0405 | HELIX | 3 | 66 | 179 | 152 | 0.4397 | BRAD | 3 | 47 | 143 | 122 | 0.2292 | GAUSS | 3 | 3 | 10 | 8 | 0.0093 | MEYER | 3 | 1 | 1 | 1 | 0.0085 | GULF | 3 | 1 | 2 | 2 | 0.0068 | BOX | 3 | 1 | 1 | 1 | 0.0600 | SING | 4 | 62 | 198 | 164 | 0.3000 | WOOD | 4 | 103 | 298 | 247 | 0.5000 | KOWOSB | 4 | 77 | 222 | 192 | 0.4000 | BD | 4 | 53 | 204 | 162 | 0.3000 | OSB1 | 5 | 1 | 1 | 1 | 0.0084 | BIGGS | 6 | 128 | 395 | 341 | 0.7000 | OSB2 | 11 | 379 | 915 | 827 | 1.2000 | JENSAM | 6 | NaN | NaN | NaN | NaN | | 7 | 12 | 51 | 32 | 0.1114 | | 8 | 11 | 50 | 26 | 0.0844 | | 9 | NaN | NaN | NaN | NaN | | 10 | 5 | 59 | 34 | 0.0421 | | 11 | 21 | 148 | 105 | 0.1749 | VARDIM | 3 | 4 | 40 | 26 | 0.0246 | | 5 | 6 | 57 | 38 | 0.0174 | | 6 | 5 | 65 | 43 | 0.0266 | | 8 | 7 | 72 | 47 | 0.0226 | | 9 | 7 | 78 | 50 | 0.0323 | | 10 | 7 | 81 | 52 | 0.0495 | | 12 | 7 | 90 | 58 | 0.0610 | | 15 | 8 | 92 | 60 | 0.0583 | WATSON | 5 | 135 | 391 | 336 | 0.5000 | | 6 | 421 | 1186 | 1043 | 1.4000 | | 7 | 1822 | 5278 | 4655 | 6.0000 | | 8 | 2607 | 7589 | 6716 | 9.0000 | | 10 | NaN | NaN | NaN | NaN | | 12 | 3370 | 10111 | 8930 | 12.0000 | | 15 | 5749 | 17442 | 15368 | 27.0000 | | 20 | 5902 | 18524 | 16282 | 36.0000 | PEN2 | 5 | 128 | 485 | 421 | 1.1000 | | 10 | 147 | 571 | 486 | 0.4000 | | 15 | 663 | 2262 | 1996 | 2.0000 | | 20 | 734 | 2452 | 2181 | 3.0000 | | 30 | 810 | 2438 | 2264 | 3.0000 | | 40 | 1488 | 4502 | 3960 | 5.0000 | | 50 | 744 | 2342 | 2056 | 2.0000 | | 60 | 755 | 2457 | 2188 | 3.0000 | PEN1 | 5 | 39 | 174 | 141 | 0.3298 | | 10 | 94 | 404 | 345 | 0.8000 | | 20 | 35 | 162 | 127 | 0.1220 | | 30 | 76 | 347 | 286 | 0.3000 | | 50 | 81 | 375 | 313 | 0.4000 | | 100 | 31 | 184 | 141 | 0.2434 | | 200 | 25 | 171 | 126 | 0.4106 | | 300 | 26 | 186 | 139 | 1.0160 | TRIG | 10 | 33 | 74 | 65 | 0.2781 | | 20 | 60 | 145 | 132 | 0.5098 | | 50 | 43 | 95 | 90 | 0.3620 | | 100 | 53 | 116 | 108 | 0.4474 | | 200 | 59 | 123 | 118 | 1.7555 | | 300 | 51 | 109 | 105 | 12.6411 | | 400 | 58 | 121 | 114 | 45.0506 | | 500 | 51 | 110 | 105 | 89.7073 | ROSEX | 100 | 36 | 124 | 99 | 0.1230 | | 200 | 34 | 125 | 102 | 0.1539 | | 300 | 35 | 133 | 107 | 0.3939 | | 400 | 31 | 121 | 100 | 0.5789 | | 500 | 31 | 129 | 106 | 0.8736 | | 1000 | 37 | 142 | 116 | 3.5602 | | 1500 | 34 | 132 | 106 | 7.4845 | | 2000 | 32 | 128 | 103 | 12.9238 | SINGX | 100 | 77 | 227 | 187 | 0.7000 | | 200 | 54 | 172 | 141 | 0.2376 | | 300 | 99 | 301 | 248 | 1.0000 | | 400 | 79 | 245 | 207 | 1.3000 | | 500 | 69 | 215 | 180 | 1.6000 | | 1000 | 101 | 322 | 271 | 8.6000 | | 1500 | 66 | 210 | 174 | 12.4000 | | 2000 | 69 | 210 | 173 | 23.1000 | BV | 200 | NaN | NaN | NaN | NaN | | 300 | NaN | NaN | NaN | NaN | | 400 | 4509 | 8044 | 8043 | 63.0000 | | 500 | 1635 | 2926 | 2925 | 34.0000 | | 600 | 925 | 1605 | 1604 | 24.5000 | | 1000 | 247 | 418 | 417 | 16.9000 | | 1500 | 18 | 38 | 37 | 3.0564 | | 2000 | 2 | 6 | 5 | 0.6883 | IE | 200 | 7 | 15 | 8 | 0.3546 | | 300 | 7 | 15 | 8 | 0.7927 | | 400 | 7 | 15 | 8 | 1.4039 | | 500 | 7 | 15 | 8 | 2.2022 | | 600 | 7 | 15 | 8 | 3.1297 | | 1000 | 7 | 15 | 8 | 8.6892 | | 1500 | 7 | 15 | 8 | 19.5607 | | 2000 | 7 | 15 | 8 | 34.7423 | TRID | 200 | 31 | 70 | 58 | 0.2691 | | 300 | 33 | 73 | 65 | 0.2857 | | 400 | 32 | 72 | 61 | 0.4264 | | 500 | 34 | 76 | 71 | 0.6853 | | 600 | 35 | 78 | 74 | 0.9484 | | 1000 | 36 | 81 | 77 | 2.6056 | | 1500 | 36 | 84 | 79 | 5.8397 | | 2000 | 35 | 80 | 73 | 9.9491 |
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