Intersecting and dense restrictions of clutters in polynomial time

IF 2.2 2区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Mathematical Programming Pub Date : 2023-12-18 DOI:10.1007/s10107-023-02034-3
Martin Drees
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Abstract

A clutter is a family of sets, called members, such that no member contains another. It is called intersecting if every two members intersect, but not all members have a common element. Dense clutters additionally do not have a fractional packing of value 2. We are looking at certain substructures of clutters, namely minors and restrictions. For a family of clutters we introduce a general sufficient condition such that for every clutter we can decide whether the clutter has a restriction in that set in polynomial time. It is known that the sets of intersecting and dense clutters satisfy this condition. For intersecting clutters we generalize the statement to k-wise intersecting clutters using a much simpler proof. We also give a simplified proof that a dense clutter with no proper dense minor is either a delta or the blocker of an extended odd hole. This simplification reduces the running time of the algorithm for finding a delta or the blocker of an extended odd hole minor from previously \({\mathscr {O}}(n^4)\) to \({\mathscr {O}}(n^3)\) filter oracle calls.

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在多项式时间内对杂乱无章的相交和密集进行限制
杂集是一组集合(称为成员),其中没有任何成员包含另一个成员。如果每两个成员相交,但并非所有成员都有一个共同元素,则称为相交集合。此外,密集杂乱集没有值为 2 的分数包装。我们正在研究杂乱无章的某些子结构,即最小值和限制值。对于一个凌乱族,我们引入了一个一般充分条件,即对于每一个凌乱族,我们都能在多项式时间内判定该凌乱族在该集合中是否有限制条件。众所周知,相交杂波集和密集杂波集都满足这一条件。对于相交杂乱集,我们用更简单的证明将其推广到 k 向相交杂乱集。我们还给出了一个简化证明,即一个没有适当密集次要粒子的密集杂波要么是一个三角洲,要么是一个扩展奇数孔的阻塞体。这种简化减少了算法的运行时间,使其从以前的 \({\mathscr {O}}(n^4)\) 滤波器调用 \({\mathscr {O}}(n^3)\) 减少到 \({\mathscr {O}}(n^3)\) 滤波器调用。
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来源期刊
Mathematical Programming
Mathematical Programming 数学-计算机:软件工程
CiteScore
5.70
自引率
11.10%
发文量
160
审稿时长
4-8 weeks
期刊介绍: Mathematical Programming publishes original articles dealing with every aspect of mathematical optimization; that is, everything of direct or indirect use concerning the problem of optimizing a function of many variables, often subject to a set of constraints. This involves theoretical and computational issues as well as application studies. Included, along with the standard topics of linear, nonlinear, integer, conic, stochastic and combinatorial optimization, are techniques for formulating and applying mathematical programming models, convex, nonsmooth and variational analysis, the theory of polyhedra, variational inequalities, and control and game theory viewed from the perspective of mathematical programming.
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