Research Article
Global Convergence of a Nonlinear Conjugate Gradient Method
Table 2
The numerical results of the VPRP method.
| Problem | Dim | NI | NF | NG | CPU |
| ROSE | 2 | 24 | 111 | 85 | 0.1585 | FROTH | 2 | 12 | 78 | 59 | 0.0698 | BADSCP | 2 | 99 | 394 | 336 | 0.4000 | BADSCB | 2 | 13 | 30 | 19 | 0.0448 | BEALE | 2 | 12 | 48 | 35 | 0.0402 | HELIX | 3 | 74 | 203 | 175 | 0.5000 | BRAD | 3 | 30 | 100 | 80 | 0.2027 | GAUSS | 3 | 3 | 7 | 4 | 0.0085 | MEYER | 3 | 1 | 1 | 1 | 0.0058 | GULF | 3 | 1 | 2 | 2 | 0.0052 | BOX | 3 | 1 | 1 | 1 | 0.0561 | SING | 4 | 101 | 341 | 289 | 0.7000 | WOOD | 4 | 174 | 482 | 417 | 0.6000 | KOWOSB | 4 | 71 | 234 | 203 | 0.3000 | BD | 4 | 42 | 161 | 125 | 0.2451 | OSB1 | 5 | 1 | 1 | 1 | 0.0063 | BIGGS | 6 | 113 | 375 | 330 | 0.8000 | OSB2 | 11 | 264 | 667 | 603 | 1.5000 | JENSAM | 6 | 9 | 33 | 17 | 0.0696 | | 7 | 11 | 39 | 17 | 0.1137 | | 8 | 10 | 42 | 19 | 0.0883 | | 9 | 17 | 90 | 57 | 0.1850 | | 10 | 17 | 124 | 84 | 0.1435 | | 11 | 6 | 76 | 46 | 0.1111 | VARDIM | 3 | 4 | 40 | 26 | 0.0290 | | 5 | 6 | 57 | 38 | 0.0203 | | 6 | 5 | 65 | 43 | 0.0273 | | 8 | 7 | 72 | 47 | 0.036 | | 9 | 7 | 78 | 50 | 0.0715 | | 10 | 7 | 81 | 52 | 0.0458 | | 12 | 7 | 90 | 58 | 0.0672 | | 15 | 8 | 92 | 60 | 0.0622 | WATSON | 5 | 193 | 535 | 473 | 1.4000 | | 6 | 342 | 1002 | 890 | 2.2000 | | 7 | 1451 | 4157 | 3678 | 5.0000 | | 8 | 6720 | 19530 | 17300 | 20.0000 | | 10 | NaN | NaN | NaN | NaN | | 12 | 3507 | 10432 | 9234 | 13.0000 | | 15 | 5271 | 15817 | 14006 | 24.0000 | | 20 | NaN | NaN | NaN | NaN | PEN2 | 5 | 129 | 499 | 434 | 1.0000 | | 10 | 90 | 379 | 328 | 0.3000 | | 15 | 434 | 1467 | 1298 | 2.2000 | | 20 | 941 | 2959 | 2612 | 3.0000 | | 30 | 771 | 2531 | 2193 | 3.0000 | | 40 | 248 | 978 | 860 | 1.6000 | | 50 | 952 | 2788 | 2511 | 3.0000 | | 60 | 927 | 2822 | 2435 | 4.0000 | PEN1 | 5 | 32 | 151 | 124 | 0.3752 | | 10 | 90 | 383 | 324 | 0.8000 | | 20 | 28 | 160 | 121 | 0.1119 | | 30 | 76 | 334 | 279 | 0.3000 | | 50 | 64 | 344 | 280 | 0.5000 | | 100 | 23 | 160 | 122 | 0.2212 | | 200 | 21 | 174 | 128 | 0.4081 | | 300 | 28 | 192 | 143 | 1.0207 | TRIG | 10 | 36 | 82 | 71 | 0.4048 | | 20 | 56 | 125 | 114 | 0.6413 | | 50 | 45 | 93 | 85 | 0.3426 | | 100 | 58 | 120 | 113 | 0.4573 | | 200 | 64 | 135 | 128 | 2.0248 | | 300 | 52 | 102 | 99 | 12.4258 | | 400 | 60 | 132 | 125 | 52.5691 | | 500 | 59 | 127 | 116 | 101.6207 | ROSEX | 100 | 24 | 111 | 85 | 0.2798 | | 200 | 24 | 111 | 85 | 0.2808 | | 300 | 24 | 111 | 85 | 0.3136 | | 400 | 24 | 111 | 85 | 0.4271 | | 500 | 24 | 111 | 85 | 0.6181 | | 1000 | 24 | 111 | 85 | 2.1821 | | 1500 | 24 | 111 | 85 | 4.7644 | | 2000 | 24 | 111 | 85 | 8.8878 | SINGX | 100 | 169 | 562 | 483 | 0.6000 | | 200 | 199 | 676 | 576 | 1.4000 | | 300 | 365 | 1181 | 1031 | 3.7000 | | 400 | 627 | 2025 | 1756 | 9.0000 | | 500 | 129 | 431 | 367 | 2.5000 | | 1000 | 229 | 788 | 675 | 16.9000 | | 1500 | 100 | 329 | 280 | 16.0000 | | 2000 | 128 | 428 | 363 | 36.0000 | BV | 200 | NaN | NaN | NaN | NaN | | 300 | 7278 | 12990 | 12989 | 55.0000 | | 400 | 3837 | 6707 | 6706 | 42.0000 | | 500 | 1842 | 3236 | 3235 | 26.0000 | | 600 | 898 | 1562 | 1561 | 17.6000 | | 1000 | 133 | 232 | 231 | 6.2000 | | 1500 | 19 | 36 | 35 | 2.0484 | | 2000 | 2 | 6 | 5 | 0.5156 | IE | 200 | 7 | 15 | 8 | 0.3596 | | 300 | 7 | 15 | 8 | 0.7986 | | 400 | 7 | 15 | 8 | 1.4202 | | 500 | 7 | 15 | 8 | 2.2147 | | 600 | 7 | 15 | 8 | 3.1811 | | 1000 | 7 | 15 | 8 | 8.8316 | | 1500 | 7 | 15 | 8 | 19.7838 | | 2000 | 7 | 15 | 8 | 34.7553 | TRID | 200 | 33 | 75 | 71 | 0.3758 | | 300 | 35 | 79 | 75 | 0.3654 | | 400 | 35 | 78 | 74 | 0.3912 | | 500 | 35 | 78 | 74 | 0.5188 | | 600 | 37 | 82 | 78 | 0.7388 | | 1000 | 34 | 76 | 72 | 1.6832 | | 1500 | 37 | 86 | 81 | 4.1950 | | 2000 | 37 | 86 | 80 | 7.5255 |
|
|