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Despite being a quintessential example of a hard problem, the quest for finding fast algorithms for deciding satisfiability of propositional formulas has occupied computer scientists both in theory and in practice. In this article, we survey recent progress on designing algorithms with strong refutation guarantees forsmoothedinstances of the $k$-SAT problem. Smoothed instances are formed by slight random perturbations of arbitrary instances, and their study is a way to bridge the gap between worst-case and average-case models of problem instances. Our methods yield new algorithms for smoothed $k$-SAT instances with guarantees that match those for the significantly simpler and well-studied model ofrandomformulas. Additionally, they have led to a novel and unexpected line of attack on some longstanding extremal combinatorial problems in graph theory and coding theory. As an example, we will discuss the resolution of a 2008 conjecture of Feige on the existence of short cycles in hypergraphs. 

Abstract Given a $$k$$-uniform hypergraph $$H$$ on $$n$$ vertices, an even cover in $$H$$ is a collection of hyperedges that touch each vertex an even number of times. Even covers are a generalization of cycles in graphs and are equivalent to linearly dependent subsets of a system of linear equations modulo $$2$$. As a result, they arise naturally in the context of well-studied questions in coding theory and refuting unsatisfiable $$k$$-SAT formulas. Analogous to the irregular Moore bound of Alon, Hoory, and Linial [3], Feige conjectured [8] an extremal trade-off between the number of hyperedges and the length of the smallest even cover in a $$k$$-uniform hypergraph. This conjecture was recently settled up to a multiplicative logarithmic factor in the number of hyperedges [12, 13]. These works introduce the new technique that relates hypergraph even covers to cycles in the associated Kikuchi graphs. Their analysis of these Kikuchi graphs, especially for odd $$k$$, is rather involved and relies on matrix concentration inequalities. In this work, we give a simple and purely combinatorial argument that recovers the best-known bound for Feige’s conjecture for even $$k$$. We also introduce a novel variant of a Kikuchi graph which together with this argument improves the logarithmic factor in the best-known bounds for odd $$k$$. As an application of our ideas, we also give a purely combinatorial proof of the improved lower bounds [4] on 3-query binary linear locally decodable codes. 

A set of high dimensional points X ={x1,x2,…,xn}⊆Rd in isotropic position is said to be δ -anti concentrated if for every direction v, the fraction of points in X satisfying |⟨xi,v⟩|⩽δ is at most O(δ). Motivated by applications to list-decodable learning and clustering, three recent works [7], [44], [71] considered the problem of constructing efficient certificates of anti-concentration in the average case, when the set of points X corresponds to samples from a Gaussian distribution. Their certificates played a crucial role in several subsequent works in algorithmic robust statistics on list-decodable learning and settling the robust learnability of arbitrary Gaussian mixtures. Unlike related efficient certificates of concentration properties that are known for wide class of distri-butions [52], the aforementioned approach has been limited only to rotationally invariant distributions (and their affine transformations) with the only prominent example being Gaussian distributions. This work presents a new (and arguably the most natural) formulation for anti- concentration. Using this formulation, we give quasi-polynomial time verifiable sum-of-squares certificates of anti-concentration that hold for a wide class of non-Gaussian distributions including anti-concentrated bounded product distributions and uniform distributions over Lp balls (and their affine transformations). Consequently, our method upgrades and extends results in algorithmic robust statistics e.g., list-decodable learning and clustering, to such distributions. As in the case of previous works, our certificates are also obtained via relaxations in the sum-of-squares hierarchy. However, the nature of our argument differs significantly from prior works that formulate anti-concentration as the non-negativity of an explicit polynomial. Our argument constructs a canonical integer program for anti-concentration and analysis a SoS relaxation of it, independent of the intended application. The explicit polynomials appearing in prior works can be seen as specific dual certificates to this program. From a technical standpoint, unlike existing works that explicitly construct sum-of-squares certificates, our argument relies on duality and analyzes a pseudo-expectation on large subsets of the input points that take a small value in some direction. Our analysis uses the method of polynomial reweightings to reduce the problem to analyzing only analytically dense or sparse directions. 

A code C ∶ {0,1}k → {0,1}n is a q-locally decodable code (q-LDC) if one can recover any chosen bit bi of the message b ∈ {0,1}k with good confidence by randomly querying the encoding x = C(b) on at most q coordinates. Existing constructions of 2-LDCs achieve n = exp(O(k)), and lower bounds show that this is in fact tight. However, when q = 3, far less is known: the best constructions achieve n = exp(ko(1)), while the best known results only show a quadratic lower bound n ≥ Ω(k2/log(k)) on the blocklength. In this paper, we prove a near-cubic lower bound of n ≥ Ω(k3/log6(k)) on the blocklength of 3-query LDCs. This improves on the best known prior works by a polynomial factor in k. Our proof relies on a new connection between LDCs and refuting constraint satisfaction problems with limited randomness. Our quantitative improvement builds on the new techniques for refuting semirandom instances of CSPs and, in particular, relies on bounding the spectral norm of appropriate Kikuchi matrices.