Let $Φ$ be a uniformly random $k$-SAT formula with $n$ variables and $m$ clauses. We study the algorithmic task of finding a satisfying assignment of $Φ$. It is known that satisfying assignments exist with high probability up to clause density $m/n = 2^k \log 2 - \frac12 (\log 2 + 1) + o_k(1)$, while the best polynomial-time algorithm known, the Fix algorithm of Coja-Oghlan, finds a satisfying assignment at the much lower clause density $(1 - o_k(1)) 2^k \log k / k$. This prompts the question: is it possible to efficiently find a satisfying assignment at higher clause densities?
We prove that the class of low degree polynomial algorithms cannot find a satisfying assignment at clause density $(1 + o_k(1)) κ^* 2^k \log k / k$ for a universal constant $κ^* \approx 4.911$. This class encompasses Fix, message passing algorithms including Belief and Survey Propagation guided decimation (with bounded or mildly growing number of rounds), and local algorithms on the factor graph. This is the first hardness result for any class of algorithms at clause density within a constant factor of that achieved by Fix. Our proof establishes and leverages a new many-way overlap gap property tailored to random $k$-SAT.
Algorithmic List Decoding of Reed-Solomon Codes up to Capacity
University of California, Berkeley（加州大学伯克利分校）；Department of EECS, University of Michigan, Ann Arbor（密歇根大学安娜堡分校电气与计算机工程系）；Harvard University, Cambridge, Massachusetts（哈佛大学）；Simons Institute for the Theory of Computing, Berkeley and Institute for Advanced Study, Princeton（加州大学伯克利分校西蒙斯理论计算研究所和普林斯顿高等研究院）
