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Suppose we add a specific regular e to all even variables in the cycle, and take away the same frequent e from all odd variables while in the cycle. For any this kind of e, the sum of variables near Each and every vertex stays precisely the same (1), so the vertex constraints are still pleased. What's more, if e is sufficiently smaller, all variables continue being concerning 0 and 1, Therefore the domain constraints remain happy far too.
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It is a snap to locate a largest e that maintains the domain constraints: it can be both the smallest distinction between an odd variable and 0, or the smallest distinction between a good variable and one. Now, We have now a single fewer fractional variable, so k(x) decreases by one. The objective price continues to be the same, since or else we could raise it by choosing e to be beneficial or detrimental, in contradiction to the idea that it's maximal.
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To ensure that the variables certainly stand for a great matching, we add constraints saying that every vertex is adjacent to accurately a person edge from the matching, i.
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