Research Article
Global Convergence of a Modified Two-Parameter Scaled BFGS Method with Yuan-Wei-Lu Line Search for Unconstrained Optimization
Table 2
The numerical results for problems 1–17.
| MTPSBFGS-YWL | SBFGS-WWP | No. | Dim | NI | NFG | CPU time | NI | NFG | CPU time |
| 1 | 300 | 29 | 63 | 0.3125 | 25 | 57 | 0.3125 | 1 | 900 | 24 | 56 | 5.859375 | 23 | 54 | 5.96875 | 1 | 2700 | 22 | 48 | 86.9375 | 29 | 67 | 135.6875 | 2 | 300 | 50 | 114 | 0.59375 | 46 | 102 | 0.5 | 2 | 900 | 51 | 114 | 12.890625 | 50 | 112 | 13.515625 | 2 | 2700 | 53 | 120 | 217.90625 | 53 | 122 | 254.75 | 3 | 300 | 50 | 137 | 0.515625 | 47 | 120 | 0.5 | 3 | 900 | 75 | 215 | 17.59375 | 63 | 167 | 16.75 | 3 | 2700 | 43 | 135 | 184.5625 | 60 | 166 | 280.484375 | 4 | 300 | 104 | 370 | 1.25 | 62 | 176 | 0.75 | 4 | 900 | 87 | 258 | 22.453125 | 38 | 109 | 9.1875 | 4 | 2700 | 67 | 179 | 308.3125 | 50 | 149 | 236.984375 | 5 | 300 | 18 | 48 | 0.21875 | 22 | 58 | 0.296875 | 5 | 900 | 18 | 45 | 4.5625 | 20 | 46 | 5.171875 | 5 | 2700 | 20 | 45 | 90.25 | 23 | 58 | 106.984375 | 6 | 300 | 68 | 152 | 0.828125 | 68 | 152 | 0.796875 | 6 | 900 | 69 | 158 | 18.375 | 69 | 158 | 18.40625 | 6 | 2700 | 85 | 192 | 405.84375 | 85 | 192 | 410.765625 | 7 | 300 | 76 | 154 | 0.8125 | 76 | 154 | 0.875 | 7 | 900 | 133 | 268 | 36.6875 | 133 | 268 | 36.84375 | 7 | 2700 | 232 | 466 | 1158.734375 | 231 | 464 | 1151.625 | 8 | 300 | 22 | 49 | 0.203125 | 26 | 54 | 0.265625 | 8 | 900 | 25 | 55 | 6.515625 | 25 | 55 | 6.421875 | 8 | 2700 | 25 | 55 | 118.640625 | 25 | 55 | 116.859375 | 9 | 300 | 7 | 16 | 0.0625 | 12 | 26 | 0.109375 | 9 | 900 | 7 | 16 | 1.546875 | 12 | 26 | 2.84375 | 9 | 2700 | 8 | 18 | 31.96875 | 12 | 26 | 49.046875 | 10 | 300 | 2 | 9 | 0 | 2 | 9 | 0 | 10 | 900 | 2 | 9 | 0.0625 | 2 | 9 | 0.0625 | 10 | 2700 | 2 | 9 | 0.25 | 2 | 9 | 0.25 | 11 | 300 | 75 | 194 | 0.921875 | 46 | 94 | 0.59375 | 11 | 900 | 95 | 272 | 26.359375 | 66 | 134 | 18.484375 | 11 | 2700 | 6 | 20 | 19.875 | 97 | 196 | 472.390625 | 12 | 300 | 11 | 24 | 0.125 | 11 | 24 | 0.109375 | 12 | 900 | 13 | 28 | 3.453125 | 13 | 28 | 3.296875 | 12 | 2700 | 13 | 28 | 60.34375 | 13 | 28 | 58.40625 | 13 | 300 | 11 | 25 | 0.125 | 10 | 23 | 0.125 | 13 | 900 | 8 | 23 | 1.921875 | 10 | 26 | 2.46875 | 13 | 2700 | 19 | 94 | 88.65625 | 19 | 96 | 88.71875 | 14 | 300 | 8 | 20 | 0.125 | 8 | 20 | 0.15625 | 14 | 900 | 7 | 18 | 1.703125 | 7 | 18 | 1.703125 | 14 | 2700 | 7 | 18 | 27.6875 | 7 | 18 | 27.421875 | 15 | 300 | 19 | 43 | 0.359375 | 22 | 49 | 0.40625 | 15 | 900 | 25 | 57 | 7.125 | 39 | 82 | 11.078125 | 15 | 2700 | 25 | 55 | 119.40625 | 41 | 86 | 198.203125 | 16 | 300 | 9 | 21 | 0.109375 | 10 | 23 | 0.0625 | 16 | 900 | 8 | 18 | 1.921875 | 9 | 21 | 2.15625 | 16 | 2700 | 11 | 24 | 49.359375 | 10 | 22 | 43.046875 | 17 | 300 | 33 | 73 | 0.421875 | 32 | 75 | 0.390625 | 17 | 900 | 23 | 49 | 5.984375 | 23 | 49 | 5.8125 | 17 | 2700 | 25 | 53 | 115.890625 | 25 | 53 | 112.984375 |
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