Research Article
Global Convergence of a Nonlinear Conjugate Gradient Method
Table 4
The numerical results of the DL+ method.
| P | Dim | It | f_iter | Grade_iter | CPU |
| ROSE | 2 | 25 | 110 | 87 | 0.1192 | FROTH | 2 | 9 | 65 | 50 | 0.0283 | BADSCP | 2 | 19 | 146 | 133 | 0.1003 | BADSCB | 2 | 11 | 56 | 47 | 0.0434 | BEALE | 2 | 11 | 50 | 39 | 0.0355 | HELIX | 3 | 50 | 144 | 122 | 0.2616 | BARD | 3 | 20 | 76 | 64 | 0.0824 | GAUSS | 3 | 2 | 5 | 3 | 0.0079 | MEYER | 3 | 1 | 1 | 1 | 0.0087 | GULF | 3 | 1 | 2 | 2 | 0.0062 | BOX | 3 | 1 | 1 | 1 | 0.0601 | SING | 4 | 91 | 295 | 250 | 0.5000 | WOOD | 4 | 148 | 402 | 349 | 0.8000 | KOWOSB | 4 | 76 | 236 | 204 | 0.4000 | BD | 4 | 67 | 208 | 177 | 0.3000 | OSB1 | 5 | 1 | 1 | 1 | 0.0083 | BIGGS | 6 | 59 | 211 | 189 | 0.3000 | OSB2 | 11 | 279 | 699 | 614 | 1.4000 | JENSAM | 6 | 9 | 35 | 19 | 0.0570 | | 7 | 9 | 35 | 17 | 0.1276 | | 8 | NaN | NaN | NaN | NaN | | 9 | 15 | 91 | 57 | 0.1148 | | 10 | 17 | 136 | 96 | 0.2084 | | 11 | 5 | 80 | 50 | 0.0882 | VARDIM | 3 | 4 | 40 | 26 | 0.0153 | | 5 | 6 | 57 | 38 | 0.0192 | | 6 | 5 | 65 | 43 | 0.0184 | | 8 | 7 | 72 | 47 | 0.0219 | | 9 | 7 | 78 | 50 | 0.0438 | | 10 | 7 | 81 | 52 | 0.0242 | | 12 | 7 | 90 | 58 | 0.0442 | | 15 | 8 | 92 | 60 | 0.0535 | WASTON | 5 | 62 | 208 | 173 | 0.2000 | | 6 | 346 | 1055 | 927 | 1.8000 | | 7 | 1247 | 3443 | 3085 | 4.0000 | | 8 | 2034 | 6104 | 5408 | 7.0000 | | 10 | 5973 | 17601 | 15699 | 21.0000 | | 12 | 3200 | 9291 | 8259 | 12.0000 | | 15 | 1865 | 5402 | 4790 | 8.0000 | | 20 | 6420 | 18320 | 16360 | 30.0000 | PEN2 | 5 | 83 | 329 | 283 | 0.7000 | | 10 | 128 | 519 | 449 | 1.0000 | | 15 | 277 | 1040 | 922 | 0.9000 | | 20 | 290 | 1113 | 981 | 0.9000 | | 30 | 767 | 2231 | 2040 | 3.0000 | | 40 | 675 | 1929 | 1706 | 3.0000 | | 50 | 617 | 2223 | 1962 | 2.0000 | | 60 | NaN | NaN | NaN | NaN | PEN1 | 5 | 33 | 165 | 139 | 0.2615 | | 10 | 78 | 353 | 298 | 0.7000 | | 20 | 24 | 119 | 94 | 0.0842 | | 30 | 73 | 367 | 305 | 0.3000 | | 50 | 66 | 356 | 293 | 0.6000 | | 100 | 22 | 154 | 117 | 0.1957 | | 200 | 27 | 178 | 136 | 0.4346 | | 300 | 29 | 195 | 147 | 1.0408 | TRIG | 10 | 34 | 82 | 71 | 0.2910 | | 20 | 59 | 140 | 129 | 0.5161 | | 50 | 46 | 94 | 88 | 0.3865 | | 100 | 55 | 120 | 111 | 0.4625 | | 200 | 56 | 116 | 111 | 1.6476 | | 300 | 52 | 110 | 105 | 12.3062 | | 400 | 56 | 115 | 113 | 44.8831 | | 500 | 54 | 121 | 116 | 97.7043 | ROSEX | 100 | 25 | 110 | 87 | 0.0859 | | 200 | 25 | 110 | 87 | 0.2019 | | 300 | 25 | 110 | 87 | 0.2697 | | 400 | 25 | 110 | 87 | 0.4169 | | 500 | 25 | 110 | 87 | 0.5990 | | 1000 | 25 | 110 | 87 | 2.2166 | | 1500 | 25 | 110 | 87 | 4.8426 | | 2000 | 25 | 110 | 87 | 9.1263 | SINGX | 100 | 113 | 384 | 329 | 1.0000 | | 200 | 117 | 397 | 341 | 0.5000 | | 300 | 215 | 735 | 634 | 2.1000 | | 400 | 113 | 384 | 329 | 1.6000 | | 500 | 110 | 367 | 313 | 2.2000 | | 1000 | 137 | 452 | 388 | 9.2000 | | 1500 | 152 | 494 | 420 | 22.1000 | | 2000 | 110 | 367 | 313 | 30.2000 | BV | 200 | NaN | NaN | NaN | NaN | | 300 | NaN | NaN | NaN | NaN | | 400 | 5624 | 8458 | 8457 | 49.0000 | | 500 | 3314 | 4854 | 4853 | 38.0000 | | 600 | 1618 | 2291 | 2290 | 25.0000 | | 1000 | 258 | 312 | 311 | 8.7000 | | 1500 | 18 | 30 | 29 | 1.7251 | | 2000 | 2 | 6 | 5 | 0.5129 | IE | 200 | 6 | 13 | 7 | 0.3075 | | 300 | 6 | 13 | 7 | 0.6731 | | 400 | 6 | 13 | 7 | 1.1939 | | 500 | 6 | 13 | 7 | 1.8608 | | 600 | 6 | 13 | 7 | 2.6785 | | 1000 | 6 | 13 | 7 | 7.3685 | | 1500 | 6 | 13 | 7 | 16.5794 | | 2000 | 6 | 13 | 7 | 29.5854 | TRID | 200 | 33 | 75 | 71 | 0.2606 | | 300 | 35 | 79 | 75 | 0.2866 | | 400 | 36 | 80 | 76 | 0.3653 | | 500 | 35 | 78 | 73 | 0.4857 | | 600 | 37 | 82 | 78 | 0.6950 | | 1000 | 34 | 76 | 72 | 1.6550 | | 1500 | 37 | 86 | 81 | 4.1380 | | 2000 | 37 | 86 | 80 | 7.4702 |
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