参数化复杂性中的整数规划:五个缩影

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED Discrete Optimization Pub Date : 2022-05-01 DOI:10.1016/j.disopt.2020.100596
Tomáš Gavenčiak , Martin Koutecký , Dušan Knop
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引用次数: 15

摘要

整数规划理论的有力结果最近导致了参数化复杂性的实质性进展。然而,我们的看法是,除了求解固定维整数线性规划的Lenstra算法外,在参数化复杂性社区中,对可用工具的优势和局限性仍然知之甚少。这是可以理解的:通常很难推断出准确的运行时,甚至很难推断出FPT和XP算法之间的区别,而且有些知识只是不同社区中不成文的民间传说。我们希望在纠正这种情况方面迈出一步。为此,我们首先从参数化复杂性的角度提供了一个易于导航的整数规划算法的快速参考指南。然后,我们在三个案例研究中展示了它们的应用,获得了运行时间为f(k)poly(n)的FPT算法。•建模:由于算法结果遵循将现有算法应用于新模型,因此我们将重点从复杂性结果转移到建模结果,突出使用的常见模式和技巧。•最优性规划:在给出FPT算法后,我们感兴趣的是减少对参数的依赖;我们展示了哪些算法和技巧通常对加速有用。注意poly(n):减少f(k)通常会产生意想不到的增加poly(n)的后果;因此,我们强调了常见的权衡,并展示了如何获得两全其美。具体地说,我们考虑了在某种意义上最简单的密集图的有界邻域多样性图,并通过将它们建模为固定维凸规划、n-fold整数规划、有界对偶树宽规划、固定维不定二次规划、固定维参数整数规划和2阶段随机整数规划,展示了几种FPT算法,用于解决Capacitated支配集、和着色、Max-q-Cut和某些其他着色问题。
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Integer programming in parameterized complexity: Five miniatures

Powerful results from the theory of integer programming have recently led to substantial advances in parameterized complexity. However, our perception is that, except for Lenstra’s algorithm for solving integer linear programming in fixed dimension, there is still little understanding in the parameterized complexity community of the strengths and limitations of the available tools. This is understandable: it is often difficult to infer exact runtimes or even the distinction between FPT and XP algorithms, and some knowledge is simply unwritten folklore in a different community. We wish to make a step in remedying this situation. To that end, we first provide an easy to navigate quick reference guide of integer programming algorithms from the perspective of parameterized complexity. Then, we show their applications in three case studies, obtaining FPT algorithms with runtime f(k)poly(n). We focus on:

  • Modeling: since the algorithmic results follow by applying existing algorithms to new models, we shift the focus from the complexity result to the modeling result, highlighting common patterns and tricks which are used.

  • Optimality program: after giving an FPT algorithm, we are interested in reducing the dependence on the parameter; we show which algorithms and tricks are often useful for speed-ups.

  • Minding the poly(n): reducing f(k) often has the unintended consequence of increasing poly(n); so we highlight the common trade-offs and show how to get the best of both worlds.

Specifically, we consider graphs of bounded neighborhood diversity which are in a sense the simplest of dense graphs, and we show several FPT algorithms for Capacitated Dominating Set, Sum Coloring, Max- q -Cut, and certain other coloring problems by modeling them as convex programs in fixed dimension, n-fold integer programs, bounded dual treewidth programs, indefinite quadratic programs in fixed dimension, parametric integer programs in fixed dimension, and 2-stage stochastic integer programs.
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来源期刊
Discrete Optimization
Discrete Optimization 管理科学-应用数学
CiteScore
2.10
自引率
9.10%
发文量
30
审稿时长
>12 weeks
期刊介绍: Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
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