Research Article
Global Convergence of a Modified Two-Parameter Scaled BFGS Method with Yuan-Wei-Lu Line Search for Unconstrained Optimization
Table 5
The numerical results for problems 52–68.
| MTPSBFGS-YWL | SBFGS-WWP | No. | Dim | NI | NFG | CPU time | NI | NFG | CPU time |
| 52 | 300 | 87 | 200 | 0.875 | 87 | 200 | 1.0625 | 52 | 900 | 103 | 236 | 27.6875 | 103 | 236 | 27.875 | 52 | 2700 | 118 | 270 | 566.65625 | 118 | 270 | 567.828125 | 53 | 300 | 938 | 2375 | 12.09375 | 388 | 778 | 4.671875 | 53 | 900 | 1000 | 2537 | 282.375 | 1000 | 2002 | 280.203125 | 53 | 2700 | 1000 | 2539 | 5006.875 | 1000 | 2002 | 5001.984375 | 54 | 300 | 31 | 72 | 0.328125 | 35 | 88 | 0.390625 | 54 | 900 | 42 | 107 | 10.8125 | 24 | 65 | 6.09375 | 54 | 2700 | 23 | 67 | 102.75 | 27 | 69 | 121.75 | 55 | 300 | 9 | 20 | 0.125 | 18 | 38 | 0.265625 | 55 | 900 | 10 | 22 | 2.234375 | 20 | 42 | 4.921875 | 55 | 2700 | 11 | 24 | 46.3125 | 21 | 44 | 92.390625 | 56 | 300 | 1000 | 2607 | 13.03125 | 375 | 750 | 4.5625 | 56 | 900 | 1000 | 2540 | 268.3125 | 1000 | 2000 | 277.515625 | 56 | 2700 | 1000 | 2539 | 4518.96875 | 1000 | 2000 | 4637.546875 | 57 | 300 | 107 | 260 | 1.578125 | 59 | 124 | 0.796875 | 57 | 900 | 99 | 236 | 28.140625 | 85 | 176 | 24.15625 | 57 | 2700 | 63 | 149 | 313.140625 | 105 | 216 | 522.734375 | 58 | 300 | 97 | 233 | 1.3125 | 69 | 144 | 0.890625 | 58 | 900 | 120 | 292 | 33.84375 | 99 | 204 | 27.828125 | 58 | 2700 | 144 | 344 | 717.328125 | 117 | 240 | 583.078125 | 59 | 300 | 24 | 73 | 0.25 | 76 | 169 | 0.890625 | 59 | 900 | 27 | 72 | 7.296875 | 27 | 71 | 7.296875 | 59 | 2700 | 31 | 78 | 148.625 | 79 | 173 | 389.4375 | 60 | 300 | 109 | 260 | 1.484375 | 60 | 126 | 0.796875 | 60 | 900 | 132 | 338 | 37.484375 | 87 | 180 | 24.359375 | 60 | 2700 | 176 | 434 | 882.109375 | 116 | 238 | 581.953125 | 61 | 300 | 104 | 246 | 1.53125 | 59 | 124 | 0.796875 | 61 | 900 | 96 | 225 | 26.609375 | 85 | 176 | 24.1875 | 61 | 2700 | 66 | 154 | 324.140625 | 105 | 216 | 521.9375 | 62 | 300 | 104 | 259 | 1.390625 | 61 | 146 | 0.8125 | 62 | 900 | 89 | 232 | 25.265625 | 99 | 220 | 27.625 | 62 | 2700 | 126 | 314 | 623.578125 | 105 | 226 | 521.1875 | 63 | 300 | 143 | 362 | 2.046875 | 147 | 308 | 2.046875 | 63 | 900 | 97 | 259 | 27.59375 | 173 | 365 | 49.28125 | 63 | 2700 | 186 | 465 | 933.5 | 199 | 422 | 1005.09375 | 64 | 300 | 40 | 88 | 0.53125 | 45 | 98 | 0.578125 | 64 | 900 | 28 | 62 | 7.078125 | 32 | 70 | 8.296875 | 64 | 2700 | 30 | 66 | 135.65625 | 33 | 72 | 150.015625 | 65 | 300 | 22 | 48 | 0.359375 | 22 | 48 | 0.4375 | 65 | 900 | 19 | 45 | 5.046875 | 19 | 45 | 5.1875 | 65 | 2700 | 18 | 40 | 79.125 | 18 | 40 | 80.765625 | 66 | 300 | 611 | 1233 | 11.171875 | 618 | 1238 | 11.6875 | 66 | 900 | 1000 | 2003 | 284.8125 | 1000 | 2002 | 286.65625 | 66 | 2700 | 1000 | 2003 | 4583.015625 | 1000 | 2002 | 4587.296875 | 67 | 300 | 6 | 21 | 0 | 6 | 21 | 0 | 67 | 900 | 12 | 33 | 0.25 | 12 | 33 | 0.21875 | 67 | 2700 | 10 | 29 | 1.203125 | 13 | 59 | 1.421875 | 68 | 300 | 24 | 50 | 0.234375 | 29 | 60 | 0.328125 | 68 | 900 | 25 | 52 | 6.453125 | 31 | 64 | 8.375 | 68 | 2700 | 27 | 56 | 126.4375 | 33 | 68 | 156.328125 |
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