Research Article
Global Convergence of a Modified Two-Parameter Scaled BFGS Method with Yuan-Wei-Lu Line Search for Unconstrained Optimization
Table 3
The numerical results for problems 18–34.
| MTPSBFGS-YWL | SBFGS-WWP | No. | Dim | NI | NFG | CPU time | NI | NFG | CPU time |
| 18 | 300 | 3 | 10 | 0 | 3 | 10 | 0 | 18 | 900 | 3 | 10 | 0.53125 | 3 | 10 | 0.5 | 18 | 2700 | 3 | 10 | 9.09375 | 3 | 10 | 8.75 | 19 | 300 | 3 | 10 | 0 | 3 | 10 | 0.0625 | 19 | 900 | 3 | 10 | 0.53125 | 3 | 10 | 0.484375 | 19 | 2700 | 3 | 10 | 8.578125 | 3 | 10 | 8.421875 | 20 | 300 | 33 | 71 | 0.34375 | 32 | 69 | 0.359375 | 20 | 900 | 12 | 31 | 2.234375 | 12 | 31 | 2.171875 | 20 | 2700 | 12 | 35 | 44.546875 | 43 | 93 | 191.3125 | 21 | 300 | 25 | 56 | 0.28125 | 34 | 74 | 0.390625 | 21 | 900 | 25 | 56 | 6.703125 | 35 | 76 | 9.265625 | 21 | 2700 | 26 | 58 | 124.484375 | 36 | 78 | 170.34375 | 22 | 300 | 8 | 30 | 0.109375 | 8 | 31 | 0.125 | 22 | 900 | 8 | 31 | 1.90625 | 8 | 31 | 2 | 22 | 2700 | 8 | 31 | 33.515625 | 8 | 31 | 33.359375 | 23 | 300 | 34 | 85 | 0.359375 | 52 | 109 | 0.5625 | 23 | 900 | 39 | 89 | 10.65625 | 51 | 117 | 13.6875 | 23 | 2700 | 47 | 111 | 233.65625 | 47 | 112 | 223.71875 | 24 | 300 | 33 | 162 | 0.296875 | 29 | 145 | 0.234375 | 24 | 900 | 14 | 111 | 1.03125 | 37 | 171 | 6.890625 | 24 | 2700 | 14 | 111 | 15.34375 | 15 | 114 | 24.234375 | 25 | 300 | 90 | 262 | 1.0625 | 127 | 348 | 1.5 | 25 | 900 | 123 | 346 | 33.765625 | 88 | 257 | 23.703125 | 25 | 2700 | 56 | 139 | 270.703125 | 97 | 284 | 463.609375 | 26 | 300 | 56 | 134 | 0.640625 | 56 | 134 | 0.609375 | 26 | 900 | 65 | 152 | 17.359375 | 65 | 152 | 17.296875 | 26 | 2700 | 61 | 146 | 292.546875 | 61 | 146 | 287.25 | 27 | 300 | 6 | 16 | 0.0625 | 6 | 16 | 0 | 27 | 900 | 11 | 31 | 2.75 | 11 | 31 | 2.65625 | 27 | 2700 | 16 | 45 | 75.3125 | 17 | 44 | 79.671875 | 28 | 300 | 28 | 63 | 0.3125 | 27 | 61 | 0.296875 | 28 | 900 | 28 | 63 | 7.34375 | 27 | 62 | 6.921875 | 28 | 2700 | 25 | 61 | 119.5 | 25 | 61 | 117.265625 | 29 | 300 | 4 | 17 | 0.0625 | 4 | 15 | 0.046875 | 29 | 900 | 4 | 17 | 0.6875 | 4 | 17 | 0.828125 | 29 | 2700 | 4 | 17 | 9.484375 | 4 | 17 | 9.53125 | 30 | 300 | 93 | 188 | 1.203125 | 93 | 188 | 1.046875 | 30 | 900 | 164 | 330 | 45.96875 | 166 | 334 | 46.890625 | 30 | 2700 | 295 | 592 | 1476.90625 | 294 | 590 | 1474.96875 | 31 | 300 | 23 | 52 | 0.21875 | 23 | 52 | 0.21875 | 31 | 900 | 27 | 62 | 6.828125 | 27 | 62 | 6.84375 | 31 | 2700 | 28 | 64 | 126.734375 | 28 | 64 | 126.421875 | 32 | 300 | 22 | 46 | 0.28125 | 22 | 46 | 0.25 | 32 | 900 | 20 | 44 | 5.171875 | 20 | 44 | 5.03125 | 32 | 2700 | 47 | 98 | 219.515625 | 47 | 98 | 218.78125 | 33 | 300 | 5 | 11 | 0.0625 | 5 | 11 | 0.0625 | 33 | 900 | 4 | 9 | 0.625 | 4 | 9 | 0.59375 | 33 | 2700 | 3 | 7 | 7.421875 | 3 | 7 | 7.34375 | 34 | 300 | 3 | 7 | 0.0625 | 3 | 7 | 0 | 34 | 900 | 3 | 7 | 0.53125 | 3 | 7 | 0.5 | 34 | 2700 | 4 | 8 | 13.203125 | 4 | 8 | 13.234375 |
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