Research Article
Global Convergence of a Nonlinear Conjugate Gradient Method
Table 1
The numerical results of the modified PRP method.
| Problem | Dim | NI | NF | NG | CPU |
| ROSE | 2 | 24 | 109 | 90 | 0.3651 | FROTH | 2 | 11 | 80 | 61 | 0.0594 | BADSCP | 2 | 26 | 227 | 210 | 0.2000 | BADSCB | 2 | 11 | 89 | 79 | 0.1085 | BEALE | 2 | 21 | 75 | 59 | 0.1449 | HELIX | 3 | 25 | 76 | 61 | 0.1754 | BRAD | 3 | 20 | 73 | 61 | 0.1380 | GAUSS | 3 | 3 | 8 | 6 | 0.0164 | MEYER | 3 | 1 | 1 | 1 | 0.0063 | GULF | 3 | 1 | 2 | 2 | 0.0173 | BOX | 3 | 1 | 1 | 1 | 0.0574 | SING | 4 | 67 | 263 | 228 | 0.5000 | WOOD | 4 | 33 | 150 | 117 | 0.2421 | KOWOSB | 4 | 57 | 222 | 195 | 0.4000 | BD | 4 | 26 | 127 | 96 | 0.1995 | OSB1 | 5 | 1 | 1 | 1 | 0.0157 | BIGGS | 6 | 121 | 449 | 396 | 1.0000 | OSB2 | 11 | 341 | 900 | 811 | 1.3000 | JENSAM | 6 | 12 | 49 | 32 | 0.0900 | | 7 | 13 | 56 | 35 | 0.1872 | | 8 | 11 | 53 | 30 | 0.1678 | | 9 | 12 | 65 | 38 | 0.1160 | | 10 | 26 | 133 | 94 | 0.2604 | | 11 | NaN | NaN | NaN | NaN | VARDIM | 3 | 4 | 40 | 26 | 0.0135 | | 5 | 6 | 57 | 38 | 0.0296 | | 6 | 5 | 65 | 43 | 0.0270 | | 8 | 7 | 72 | 47 | 0.0327 | | 9 | 7 | 78 | 50 | 0.0647 | | 10 | 7 | 81 | 52 | 0.0646 | | 12 | 7 | 90 | 58 | 0.0647 | | 15 | 8 | 92 | 60 | 0.0948 | WATSON | 5 | 59 | 200 | 167 | 0.2000 | | 6 | 387 | 1281 | 1134 | 1.4000 | | 7 | 1768 | 5834 | 5191 | 6.0000 | | 8 | 3934 | 13373 | 11920 | 14.0000 | | 10 | 4319 | 15102 | 13451 | 17.0000 | | 12 | 1892 | 6762 | 6007 | 9.0000 | | 15 | 1527 | 5552 | 4933 | 7.0000 | | 20 | 3001 | 11308 | 10107 | 19.0000 | PEN2 | 5 | 111 | 439 | 393 | 0.4000 | | 10 | 185 | 845 | 752 | 1.5000 | | 15 | 154 | 774 | 679 | 0.5000 | | 20 | 178 | 989 | 889 | 0.6000 | | 30 | 123 | 610 | 534 | 0.4000 | | 40 | 147 | 700 | 617 | 0.5000 | | 50 | 152 | 744 | 651 | 1.2000 | | 60 | 163 | 813 | 720 | 0.7000 | PEN1 | 5 | 30 | 151 | 125 | 0.2742 | | 10 | 88 | 415 | 357 | 0.9000 | | 20 | 32 | 155 | 124 | 0.3349 | | 30 | 73 | 350 | 290 | 0.7000 | | 50 | 72 | 346 | 285 | 0.3000 | | 100 | 29 | 189 | 147 | 0.2458 | | 200 | 28 | 198 | 152 | 0.4759 | | 300 | 27 | 201 | 150 | 1.0464 | TRIG | 10 | 41 | 92 | 82 | 0.3817 | | 20 | 56 | 136 | 127 | 0.5634 | | 50 | 49 | 106 | 103 | 0.1949 | | 100 | 61 | 137 | 127 | 0.3857 | | 200 | 56 | 116 | 114 | 2.0205 | | 300 | 52 | 106 | 101 | 11.6394 | | 400 | 57 | 116 | 114 | 44.9734 | | 500 | 53 | 109 | 108 | 89.8125 | ROSEX | 100 | 26 | 123 | 103 | 0.2323 | | 200 | 26 | 123 | 103 | 0.2583 | | 300 | 26 | 123 | 103 | 0.3078 | | 400 | 26 | 123 | 103 | 0.4697 | | 500 | 26 | 123 | 103 | 0.6781 | | 1000 | 26 | 123 | 103 | 2.4474 | | 1500 | 26 | 123 | 103 | 5.3979 | | 2000 | 26 | 123 | 103 | 9.9364 | SINGX | 100 | 78 | 320 | 283 | 0.8000 | | 200 | 79 | 335 | 293 | 0.8000 | | 300 | 73 | 308 | 269 | 0.8000 | | 400 | 89 | 367 | 324 | 1.6000 | | 500 | 91 | 374 | 330 | 2.2000 | | 1000 | 93 | 385 | 342 | 8.0000 | | 1500 | 82 | 347 | 306 | 15.8000 | | 2000 | 80 | 341 | 299 | 28.4000 | BV | 200 | 1813 | 4326 | 4063 | 9.0000 | | 300 | 636 | 1501 | 1418 | 5.4000 | | 400 | 226 | 516 | 487 | 2.7000 | | 500 | 188 | 420 | 398 | 3.2000 | | 600 | 86 | 190 | 184 | 1.9000 | | 1000 | 21 | 40 | 37 | 0.9963 | | 1500 | 11 | 20 | 19 | 1.0900 | | 2000 | 2 | 6 | 5 | 0.5456 | IE | 200 | 6 | 13 | 7 | 0.3063 | | 300 | 6 | 13 | 7 | 0.6698 | | 400 | 6 | 13 | 7 | 1.1916 | | 500 | 6 | 13 | 7 | 1.8511 | | 600 | 6 | 13 | 7 | 2.6615 | | 1000 | 6 | 13 | 7 | 7.3635 | | 1500 | 6 | 13 | 7 | 16.6397 | | 2000 | 6 | 13 | 7 | 29.4927 | TRID | 200 | 35 | 81 | 74 | 0.3327 | | 300 | 36 | 83 | 75 | 0.3587 | | 400 | 37 | 83 | 75 | 0.3731 | | 500 | 35 | 78 | 73 | 0.4935 | | 600 | 36 | 80 | 76 | 0.6862 | | 1000 | 35 | 79 | 75 | 1.7180 | | 1500 | 36 | 84 | 79 | 4.0501 | | 2000 | 37 | 85 | 79 | 7.5866 |
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