[英]compute the tableau's nonbasic term in SCIP separator
In traditional Simplex Algorithm notation, we have x at the current basis selection B as so:在传统的单纯形算法表示法中,我们在当前基础选择 B 处有 x,如下所示:
x B = A B -1 b - A B -1 A N x N . x B = A B -1 b - A B -1 A N x N 。 How can I compute the A B -1 A N term inside a separator in SCIP, or at least iterate over its columns?
我如何计算 SCIP 中分隔符内的 A B -1 A N项,或者至少迭代它的列?
I see three helpful methods: getLPColsData
, getLPRowsData
, getLPBasisInd
.我看到三个有用的方法:
getLPColsData
、 getLPRowsData
、 getLPBasisInd
。 I'm just not sure exactly what data those methods represent, particularly the last one, with its negative row indexes.我只是不确定这些方法到底代表什么数据,尤其是最后一个,它的行索引为负。 How do I use those to get the value I want?
我如何使用它们来获得我想要的价值?
Do those methods return the same data no matter what LP algorithm is used?无论使用何种 LP 算法,这些方法都返回相同的数据吗? Or do I need to account for dual vs primal?
还是我需要考虑双重与原始? How does the use of the "revised" algorithm play into my calculation?
“修订”算法的使用如何影响我的计算?
Update: I discovered the getLPBInvARow
and getLPBInvRow
.更新:我发现了
getLPBInvARow
和getLPBInvRow
。 That seems to be much closer to what I'm after.这似乎更接近我所追求的。 I don't yet understand their results;
我还不明白他们的结果; they seem to include more/less dimensions than expected.
它们似乎包含比预期更多/更少的维度。 I'm still looking for understanding at how to use them to get the rays away from the corner.
我仍在寻求了解如何使用它们使光线远离角落。
you are correct that getLPBInvRow
or getLPBInvARow
are the methods you want.你是正确的,
getLPBInvRow
或getLPBInvARow
是你想要的方法。 getLPBInvARow
directly returns you a of the simplex tableau, but it is not more efficient to use than getLPBInvRow
and doing the multiplication yourself since the LP solver needs to also compute the actual tableau first. getLPBInvARow
直接返回一个单纯形画面,但使用起来并不比getLPBInvRow
和自己进行乘法更有效,因为 LP 求解器还需要首先计算实际画面。
I suggest you look into either sepa_gomory.c
or sepa_gmi.c
for examples of how to use these methods.我建议您查看
sepa_gomory.c
或sepa_gmi.c
以获取有关如何使用这些方法的示例。 How do they include less dimensions than expected?它们如何包含比预期更少的维度? They both return sparse vectors.
它们都返回稀疏向量。
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