arXivDaily arXiv每日学术速递 周一至周五更新
北美退伍军人节(Veterans Day),12 日无更新
arXiv 2301.05084cs.LOcs.CC
收藏

局部一致性作为约束满足问题之间的归约

Local consistency as a reduction between constraint satisfaction problems

Victor Dalmau, Jakub Opršal

AI总结:

针对承诺CSP需更强表达归约的问题,提出一致性归约框架,覆盖多数承诺CSP的NP难度证明归约,刻画弧一致性相关片段,还可用于设计易解算法并描述Sherali-Adams层级。

摘要:

我们研究将局部一致性方法用作约束满足问题(CSP)及其承诺版本之间的归约手段,旨在以类似于代数方法分类CSP间gadget归约的方式对这类归约进行分类。本研究的动机是承诺CSP领域需要表达能力更强的归约方式。尽管gadget归约足以提供(有限域)非承诺CSP范围内的所有必要难度结果,但在承诺CSP中需要使用更广泛的归约类别。\n我们提出了一个通用的归约框架,称之为一致性归约,它涵盖了近期用于证明承诺CSP的NP难度的大多数(若非全部)归约方法。我们证明了这类归约的一些基本性质,并通过模板的多态性刻画与弧一致性相关的一个片段,为理解一致性归约的能力迈出了第一步。除了证明难度,一致性归约还可通过归约到一个固定的易解(承诺)CSP来提供可行算法,例如归约到仿射方程组求解。在这一方向的诸多结果中,我们用归约到线性规划的一致性归约描述了著名的CSP Sherali-Adams层级。

英文摘要:

We study the use of local consistency methods as reductions between constraint satisfaction problems (CSPs), and promise version thereof, with the aim to classify these reductions in a similar way as the algebraic approach classifies gadget reductions between CSPs. This research is motivated by the requirement of more expressive reductions in the scope of promise CSPs. While gadget reductions are enough to provide all necessary hardness in the scope of (finite domain) non-promise CSP, in promise CSPs a wider class of reductions needs to be used. We provide a general framework of reductions, which we call consistency reductions, that covers most (if not all) reductions recently used for proving NP-hardness of promise CSPs. We prove some basic properties of these reductions, and provide the first steps towards understanding the power of consistency reductions by characterizing a fragment associated to arc-consistency in terms of polymorphisms of the template. In addition to showing hardness, consistency reductions can also be used to provide feasible algorithms by reducing to a fixed tractable (promise) CSP, for example, to solving systems of affine equations. In this direction, among other results, we describe the well-known Sherali-Adams hierarchy for CSP in terms of a consistency reduction to linear programming.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑