Given a two-prover game $G$ and its two satisfying labelings $\psi_\mathsf{s}$ and $\psi_\mathsf{t}$, the Label Cover Reconfiguration problem asks whether $\psi_\mathsf{s}$ can be transformed into $\psi_\mathsf{t}$ by repeatedly changing the value of a vertex while preserving any intermediate labeling satisfying $G$. We consider an optimization variant of Label Cover Reconfiguration by relaxing the feasibility of labelings, referred to as Maxmin Label Cover Reconfiguration: we are allowed to transform by passing through any non-satisfying labelings, but required to maximize the minimum fraction of satisfied edges during transformation from $\psi_\mathsf{s}$ to $\psi_\mathsf{t}$. Since the parallel repetition theorem of Raz (SIAM J. Comput., 1998), which implies NP-hardness of Label Cover within any constant factor, produces strong inapproximability results for many NP-hard problems, one may think of using Maxmin Label Cover Reconfiguration to derive inapproximability results for reconfiguration problems. We prove the following results on Maxmin Label Cover Reconfiguration, which display different trends from those of Label Cover and the parallel repetition theorem: (1) Maxmin Label Cover Reconfiguration can be approximated within a factor of nearly $\frac{1}{4}$ for restricted graph classes, including slightly dense graphs and balanced bipartite graphs. (2) A naive parallel repetition of Maxmin Label Cover Reconfiguration does not decrease the optimal objective value. (3) Label Cover Reconfiguration on projection games can be decided in polynomial time. The above results suggest that a reconfiguration analogue of the parallel repetition theorem is unlikely.
翻译:给定一个二人博弈 $G$ 和它的两个可满足标签 $\psi_\mathsf{s}$ 和 $\psi_\mathsf{t}$,标签覆盖重新配置问题询问是否可以通过重复改变一个节点的值,而保持任何中间满足 $G$ 的标记,将 $\psi_\mathsf{s}$ 转换为 $\psi_\mathsf{t}$。本文考虑了标签覆盖重新配置的优化变体,通过放松标记的可满足性,称为最大最小标签覆盖重新配置,我们允许通过任何不满足标记来进行转换,但在从 $\psi_\mathsf{s}$ 到 $\psi_\mathsf{t}$ 的转换过程中需要最大化满足的边的最小分数。由 Raz 的平行重复定理(SIAM J. Comput., 1998)推导,标签覆盖问题的所有常数因子内均为 NP-完全问题,从而可以考虑使用最大最小标签覆盖重新配置来导出无法近似的结果。本文证明了关于最大最小标签覆盖重新配置的以下结果,展示了与标签覆盖和平行重复定理不同的趋势:(1)带限制的图类中,包括稍微密集的图和平衡的二部图,最大最小标签覆盖重新配置可以近似于 $\frac{1}{4}$。 (2)最大最小标签覆盖重新配置的 Naive 平行重复不会降低最优目标值。 (3)关于投影博弈的标签覆盖重新配置可以在多项式时间内被决定。以上结果表明重新配置平行重复定理不太可能存在。