We study the complexity of affine Unique-Games (UG) over globally hypercontractive graphs, which are graphs that are not small set expanders but admit a useful and succinct characterization of all small sets that violate the small-set expansion property. This class of graphs includes the Johnson and Grassmann graphs, which have played a pivotal role in recent PCP constructions for UG, and their generalizations via high-dimensional expanders. Our algorithm shows how to round "low-entropy" solutions to sum-of-squares (SoS) semidefinite programs, broadly extending the algorithmic framework of [BBKSS'21]. We give a new rounding scheme for SoS, which eliminates global correlations in a given pseudodistribution so that it retains various good properties even after conditioning. Getting structural control over a pseudodistribution after conditioning is a fundamental challenge in many SoS based algorithms. Due to these challenges, [BBKSS] were not able to establish strong algorithms for globally hypercontractive graphs, and could only do so for certifiable small-set expanders. Our results improve upon the results of [BBKSS] in various aspects: we are able to deal with instances with arbitrarily small (but constant) completeness, and most importantly, their algorithm gets a soundness guarantee that degrades with other parameters of the graph (which in all PCP constructions grow with the alphabet size), whereas our doesn't. Our result suggests that UG is easy on globally hypercontractive graphs, and therefore highlights the importance of graphs that lack such a characterization in the context of PCP reductions for UG.
翻译:我们研究全局超收缩图上仿射唯一游戏(UG)的复杂性,这些图不是小集合扩展者,但却展示了违反小集合扩展性质的所有小集合的有用而简洁的特征。这类图包括约翰逊图和格拉斯曼图,它们在最近的唯一游戏PCP构造中扮演了重要角色,以及它们通过高维扩展器的推广。我们的算法展示了如何对“低熵”解进行sum-of-squares(SoS)半正定程序圆整,广泛扩展了[BBKSS'21]的算法框架。我们给出了一种新的SoS圆整方案,它消除了给定伪分布中的全局相关性,使其在条件化后仍保留各种良好性质。在条件化后获得伪分布的结构控制是许多基于SoS的算法面临的基本挑战。由于这些挑战,[BBKSS]无法为全局超收缩图建立强大的算法,只能为可证明的小集合扩展者做到这一点。我们的结果在各个方面都改进了[BBKSS]的结果:我们能够处理具有任意小(但恒定的)完成度的实例,最重要的是,他们的算法获得了一个声音保证,但随着图的其他参数(在所有PCP构造中都会随着字母表大小增长)而恶化,而我们的算法则没有。我们的结果表明,在全局超收缩图上,唯一游戏是容易解决的,因此凸显了在PCP约减中缺乏这种特征描述的图的重要性。