我有一个 MIP，我几乎可以肯定地知道解决方案。我想用 gurobi 来证明真正的解决方案（即使它不是我提供的解决方案）与我给出的解决方案的偏差不应超过 0.5%。我相信简单地继续切割而不分支可能会节省更多时间。你知道我可以简单地进行切割而不在 gurobi 中分支的方法吗？谢谢！
这是代码性能：
Changed value of parameter LogFile to
Prev: gurobi.log Default:
Changed value of parameter MIPFocus to 3
Prev: 0 Min: 0 Max: 3 Default: 0
Changed value of parameter Cuts to 3
Prev: -1 Min: -1 Max: 3 Default: -1
Optimize a model with 1794 rows, 673 columns and 4180 non zeros
Found heuristic solution: objective -22.8549
Presolve removed 18 rows and 17 columns
Presolve time: 0.01s
Presolved: 1776 rows, 656 columns, 4464 nonzeros
Loaded MIP start with objective -342.641
Variable types: 592 continuous, 64 integer (64 binary)
Presolved: 1776 rows, 656 columns, 4464 nonzeros
Root relaxation: objective -6.775689e+02, 682 iterations, 0.02 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 -677.56892 0 64 -342.64109 -677.56892 97.7% - 0s
0 0 -666.45290 0 72 -342.64109 -666.45290 94.5% - 0s
0 0 -658.68050 0 72 -342.64109 -658.68050 92.2% - 1s
0 0 -540.92023 0 72 -342.64109 -540.92023 57.9% - 3s
0 0 -503.36031 0 72 -342.64109 -503.36031 46.9% - 4s
0 0 -485.13025 0 72 -342.64109 -485.13025 41.6% - 6s
0 0 -472.73790 0 72 -342.64109 -472.73790 38.0% - 8s
0 0 -461.23185 0 72 -342.64109 -461.23185 34.6% - 9s
0 0 -453.99476 0 72 -342.64109 -453.99476 32.5% - 10s
0 0 -452.23014 0 72 -342.64109 -452.23014 32.0% - 10s
0 3 -452.23014 0 72 -342.64109 -452.23014 32.0% - 11s
642 586 -397.07656 12 54 -342.64109 -429.76289 25.4% 120 15s
1425 1290 -397.34606 11 60 -342.64109 -422.53417 23.3% 114 20s
1716 1553 -382.83438 18 72 -342.64109 -420.42709 22.7% 111 25s
1727 1560 -376.17473 16 72 -342.64109 -420.42709 22.7% 110 30s
1733 1564 -410.28764 10 72 -342.64109 -420.42709 22.7% 110 35s
1744 1571 -382.83438 18 72 -342.64109 -420.42709 22.7% 109 40s
1750 1577 -412.59771 12 69 -342.64109 -416.84728 21.7% 113 45s
1817 1602 -380.32997 19 60 -342.64109 -404.73090 18.1% 120 50s
2618 2045 -375.99924 18 62 -342.64109 -391.32863 14.2% 126 55s
3159 2315 -369.40052 22 59 -342.64109 -386.33088 12.8% 127 60s
3808 2595 -362.27693 20 60 -342.64109 -382.29310 11.6% 127 65s
4503 2903 -350.90325 24 54 -342.64109 -379.52932 10.8% 126 71s
4895 3078 -349.90847 23 55 -342.64109 -378.33598 10.4% 126 78s
5339 3242 -363.26836 21 59 -342.64109 -376.77299 10.0% 126 80s
6421 3664 -366.32746 21 56 -342.64109 -374.20072 9.21% 126 85s
7560 4450 -357.93456 21 59 -342.64109 -371.61876 8.46% 126 90s
8849 5297 -355.57657 21 59 -342.64109 -369.33074 7.79% 125 95s
10004 6042 -357.02223 24 55 -342.64109 -367.63772 7.30% 124 100s
11274 6819 -352.14570 23 55 -342.64109 -365.95440 6.80% 122 105s
12362 7437 -357.95155 22 55 -342.64109 -364.73335 6.45% 122 110s
13134 7882 -352.18831 25 47 -342.64109 -363.91508 6.21% 121 115s
...