We give a new framework based on graph regularity lemmas, for list decoding and list recovery of codes based on spectral expanders. Using existing algorithms for computing regularity decompositions of sparse graphs in (randomized) near-linear time, and appropriate choices for the constant-sized inner/base codes, we prove the following:–Expander-based codes constructed using the distance amplification technique of Alon, Edmonds and Luby [FOCS 1995] can be list decoded to capacity in near-linear time. By known results, the output list is optimal up to constant factors.–The same codes of Alon, Edmonds and Luby, can also be list recovered to capacity in near-linear time, with constant-sized output lists.–The Tanner code construction of Sipser and Spielman [IEEE Trans. Inf. Theory 1996] can be list decoded to its distance in near-linear time, with constant-sized output lists.Our results imply novel combinatorial as well as algorithmic bounds for each of the above explicit constructions. All of these bounds are obtained via combinatorial rigidity phenomena, proved using (weak) graph regularity. The regularity framework allows us to lift the list decoding and list recovery properties for the local base codes, to the global codes obtained via the above constructions.

A set of high dimensional points X$= \{x_{1},x_{2},\ldots,x_{n}\}\subseteq \mathbb{R}^{d}$in isotropic position is said to be$\delta$-anti concentrated if for every direction$v$, the fraction of points in$X$satisfying$\left|\left\langle x_i, v\right\rangle\right| \leqslant \delta$is at most$O(\delta)$. 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$L_{p}$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.

We develop new list decoding algorithms for Tanner codes and distance-amplified codes based on bipartite spectral expanders. We show that proofs exhibiting lower bounds on the minimum distance of these codes can be used as certificates discoverable by relaxations in the Sum-of-Squares (SoS) semi-definite programming hierarchy. Combining these certificates with certain entropic proxies to ensure that the solutions to the relaxations cover the entire list, then leads to algorithms for list decoding several families of codes up to the Johnson bound. We prove the following results:- We show that the LDPC Tanner codes of Zémor [IEEE Trans. Inf. Theory 2001] with alphabet size q, block-length n and distance $\delta$, based on an expander graph with degree d, can be list-decoded up to distance $\mathcal{J}_{q}(\delta)-\varepsilon$ in time $n^{O_{d, q}\left(1 / \varepsilon^{4}\right)}$, where $\mathcal{J}_{q}(\delta)$ denotes the Johnson bound.- We show that the codes obtained via the expander-based distance amplification procedure of Alon, Edmonds and Luby [FOCS 1995] can be list-decoded close to the Johnson bound using the SoS hierarchy, by reducing the list decoding problem to unique decoding of the base code. In particular, starting from any base code unique-decodable up to distance $\delta$, one can obtain near-MDS codes with rate R and distance $1-R-\varepsilon$, list-decodable up to the Johnson bound in time $n^{O_{\varepsilon, \delta}(1)}$.- We show that the locally testable codes of Dinur et al. [STOC 2022] with alphabet size q, block-length n and distance $\delta$ based on a square Cayley complex with generator sets of size d, can be list-decoded up to distance $\mathcal{J}_{q}(\delta)-\varepsilon$ in time $n^{O_{d, q}\left(1 / \varepsilon^{4}\right)}$, where $\mathcal{J}_{q}(\delta)$ denotes the Johnson bound.

Analyzing concentration of large random matrices is a common task in a wide variety of fields. Given independent random variables, several tools are available to bound the norms of random matrices whose entries are linear in the variables, such as the matrix-Bernstein inequality. However, for many recent applications, we need to bound the norms of random matrices whose entries are polynomials in the variables. Such matrices arise naturally in the analysis of spectral algorithms (e.g., Hopkins et al. [STOC 2016], Moitra and Wein [STOC 2019]), and in lower bounds for semidefinite programs based on the Sum-of-Squares (SoS) hierarchy (e.g. Barak et al. [FOCS 2016], Jones et al. [FOCS 2021]). In this work, we present a general framework to obtain such bounds, based on the beautiful matrix Efron-Stein inequalities developed by Paulin, Mackey and Tropp [Annals of Probability 2016]. The Efron- Stein inequality bounds the norm of a random matrix by the norm of another potentially simpler (but still random) matrix. We view the latter matrix as arising by “differentiating” the starting matrix. By recursively differentiating, our framework reduces the main task to bounding the norms of far simpler matrices. These simpler matrices are in fact deterministic matrices in the case of Rademacher random variables and hence, bounding their norm is a far easier task. In general for non-Rademacher random variables, the task reduces to the much easier task of scalar concentration. Moreover, in the setting of polynomial matrices, our main result also generalizes the work of Paulin, Mackey and Tropp. As applications of our basic framework, we recover known bounds in the literature, especially for simple “tensor networks” and “dense graph matrices”. As applications of our general framework, we derive bounds for “sparse graph matrices”. The sparse graph matrix bounds were obtained only recently by Jones et al. [FOCS 2021] using a nontrivial application of the trace power method, and was a core component in their work. We expect this framework will also be helpful for other applications involving concentration phenomena for nonlinear random matrices.

Abstract. We study the problem of computing the [Formula: see text] norm of a matrix [Formula: see text], defined as [Formula: see text]. This problem generalizes the spectral norm of a matrix ([Formula: see text]) and the Grothendieck problem ([Formula: see text], [Formula: see text]) and has been widely studied in various regimes. When [Formula: see text], the problem exhibits a dichotomy: constant factor approximation algorithms are known if [Formula: see text], and the problem is hard to approximate within almost polynomial factors when [Formula: see text]. The regime when [Formula: see text], known as hypercontractive norms, is particularly significant for various applications but much less well understood. The case with [Formula: see text] and [Formula: see text] was studied by Barak et al. [ Proceedings of the 44 th Annual ACM Symposium on Theory of Computing, 2012, pp. 307–326], who gave subexponential algorithms for a promise version of the problem (which captures small-set expansion) and also proved hardness of approximation results based on the exponential time hypothesis. However, no NP-hardness of approximation is known for these problems for any [Formula: see text]. We prove the first NP-hardness result (under randomized reductions) for approximating hypercontractive norms. We show that for any [Formula: see text] with [Formula: see text], [Formula: see text] is hard to approximate within [Formula: see text] assuming [Formula: see text]. En route to the above result, we also prove almost tight results for the case when [Formula: see text] with [Formula: see text].

For an abelian group H acting on the set [𝓁], an (H,𝓁)-lift of a graph G₀ is a graph obtained by replacing each vertex by 𝓁 copies, and each edge by a matching corresponding to the action of an element of H. Expanding graphs obtained via abelian lifts, form a key ingredient in the recent breakthrough constructions of quantum LDPC codes, (implicitly) in the fiber bundle codes by Hastings, Haah and O'Donnell [STOC 2021] achieving distance Ω̃(N^{3/5}), and in those by Panteleev and Kalachev [IEEE Trans. Inf. Theory 2021] of distance Ω(N/log(N)). However, both these constructions are non-explicit. In particular, the latter relies on a randomized construction of expander graphs via abelian lifts by Agarwal et al. [SIAM J. Discrete Math 2019]. In this work, we show the following explicit constructions of expanders obtained via abelian lifts. For every (transitive) abelian group H ⩽ Sym(𝓁), constant degree d ≥ 3 and ε > 0, we construct explicit d-regular expander graphs G obtained from an (H,𝓁)-lift of a (suitable) base n-vertex expander G₀ with the following parameters: ii) λ(G) ≤ 2√{d-1} + ε, for any lift size 𝓁 ≤ 2^{n^{δ}} where δ = δ(d,ε), iii) λ(G) ≤ ε ⋅ d, for any lift size 𝓁 ≤ 2^{n^{δ₀}} for a fixed δ₀ > 0, when d ≥ d₀(ε), or iv) λ(G) ≤ Õ(√d), for lift size "exactly" 𝓁 = 2^{Θ(n)}. As corollaries, we obtain explicit quantum lifted product codes of Panteleev and Kalachev of almost linear distance (and also in a wide range of parameters) and explicit classical quasi-cyclic LDPC codes with wide range of circulant sizes. Items (i) and (ii) above are obtained by extending the techniques of Mohanty, O'Donnell and Paredes [STOC 2020] for 2-lifts to much larger abelian lift sizes (as a byproduct simplifying their construction). This is done by providing a new encoding of special walks arising in the trace power method, carefully "compressing" depth-first search traversals. Result (iii) is via a simpler proof of Agarwal et al. [SIAM J. Discrete Math 2019] at the expense of polylog factors in the expansion.

The Sum-of-Squares (SoS) hierarchy of semidefinite programs is a powerful algorithmic paradigm which captures state-of-the-art algorithmic guarantees for a wide array of problems. In the average case setting, SoS lower bounds provide strong evidence of algorithmic hardness or information-computation gaps. Prior to this work, SoS lower bounds have been obtained for problems in the “dense” input regime, where the input is a collection of independent Rademacher or Gaussian random variables, while the sparse regime has remained out of reach. We make the first progress in this direction by obtaining strong SoS lower bounds for the problem of Independent Set on sparse random graphs. We prove that with high probability over an Erdós-Rénvi random graph$G\sim G_{n_{J}\frac{d}{u}}$with average degree$d > \log^{2}n$, degree-Dsos SoS fails to refute the existence of an independent set of size$k=\displaystyle \Omega(\frac{n}{\sqrt{d}(\log n)(\mathrm{D}_{\mathrm{S}\mathrm{o}\mathrm{S}})^{c_{0}}})$in$G$(where$c_{0}$is an absolute constant), whereas the true size of the largest independent set in$G$is$O(\displaystyle \frac{n\log d}{d})$. Our proof involves several significant extensions of the techniques used for proving SoS lower bounds in the dense setting. Previous lower bounds are based on the pseudo-calibration heuristic of Barak et al. [FOCS 2016] which produces a candidate SoS solution using a planted distribution indistinguishable from the input distribution via low-degree tests. In the sparse case the natural planted distribution does admit low-degree distinguishers, and we show how to adapt the pseudo-calibration heuristic to overcome this. Another notorious technical challenge for the sparse regime is the quest for matrix norm bounds. In this paper, we obtain new norm bounds for graph matrices in the sparse setting. While in the dense setting the norms of graph matrices are characterized by the size of the minimum vertex separator of the corresponding graph, this turns not to be the case for sparse graph matrices. Another contribution of our work is developing a new combinatorial understanding of structures needed to understand the norms of sparse graph matrices.

We construct an explicit family of 3XOR instances which is hard for $O(\sqrt{\log n})$ levels of the Sum-of-Squares hierarchy. In contrast to earlier constructions, which involve a random component, our systems can be constructed explicitly in deterministic polynomial time. Our construction is based on the high-dimensional expanders devised by Lubotzky, Samuels and Vishne, known as LSV complexes or Ramanujan complexes, and our analysis is based on two notions of expansion for these complexes: cosystolic expansion, and a local isoperimetric inequality due to Gromov. Our construction offers an interesting contrast to the recent work of Alev, Jeronimo and the last author~(FOCS 2019). They showed that 3XOR instances in which the variables correspond to vertices in a high-dimensional expander are easy to solve. In contrast, in our instances the variables correspond to the edges of the complex.

The Gilbert–Varshamov bound non-constructively establishes the existence of binary codes of distance 1/2−є/2 and rate Ω(є2). In a breakthrough result, Ta-Shma [STOC 2017] constructed the first explicit family of nearly optimal binary codes with distance 1/2−є/2 and rate Ω(є2+α), where α → 0 as є → 0. Moreover, the codes in Ta-Shma’s construction are є-balanced, where the distance between distinct codewords is not only bounded from below by 1/2−є/2, but also from above by 1/2+є/2.

The Gilbert-Varshamov bound (non-constructively) establishes the existence of binary codes of distance 1/2-ε and rate Ω(ε2) (where an upper bound of O(ε2log(1/ε)) is known). Ta-Shma [STOC 2017] gave an explicit construction of ε-balanced binary codes, where any two distinct codewords are at a distance between 1/2-ε/2 and 1/2+ε/2, achieving a near optimal rate of Ω(ε2+β), where β→ 0 as ε→ 0. We develop unique and list decoding algorithms for (a slight modification of) the family of codes constructed by Ta-Shma, in the adversarial error model. We prove the following results for ε-balanced codes with block length N and rate Ω(ε2+β) in this family: -For all , there are explicit codes which can be uniquely decoded up to an error of half the minimum distance in time NOε,β(1). -For any fixed constant β independent of ε, there is an explicit construction of codes which can be uniquely decoded up to an error of half the minimum distance in time (log(1/ε))O(1)·NOβ(1). -For any , there are explicit ε-balanced codes with rate Ω(ε2+β) which can be list decoded up to error 1/2-ε'in time NOε,ε',β(1), where ε',β→ 0 as ε→ 0. The starting point of our algorithms is the framework for list decoding direct-sum codes develop in Alev et al. [SODA 2020], which uses the Sum-of-Squares SDP hierarchy. The rates obtained there were quasipolynomial in ε. Here, we show how to overcome the far from optimal rates of this framework obtaining unique decoding algorithms for explicit binary codes of near optimal rate. These codes are based on simple modifications of Ta-Shma's construction.

We consider families of codes obtained by “lifting” a base code through operations such as k-XOR applied to “local views” of codewords of , according to a suitable k-uniform hypergraph. The k-XOR operation yields the direct sum encoding used in works of [Ta-Shma, STOC 2017] and [Dinur and Kaufman, FOCS 2017]. We give a general framework for list decoding such lifted codes, as long as the base code admits a unique decoding algorithm, and the hypergraph used for lifting satisfies certain expansion properties. We show that these properties are indeed satisfied by the collection of length k walks on a sufficiently strong expanding graph, and by hypergraphs corresponding to high-dimensional expanders. Instantiating our framework, we obtain list decoding algorithms for direct sum liftings corresponding to the above hypergraph families. Using known connections between direct sum and direct product, we also recover (and strengthen) the recent results of Dinur et al. [SODA 2019] on list decoding for direct product liftings. Our framework relies on relaxations given by the Sum-of-Squares (SOS) SDP hierarchy for solving various constraint satisfaction problems (CSPs). We view the problem of recovering the closest codeword to a given (possibly corrupted) word, as finding the optimal solution to an instance of a CSP. Constraints in the instance correspond to edges of the lifting hypergraph, and the solutions are restricted to lie in the base code . We show that recent algorithms for (approximately) solving CSPs on certain expanding hypergraphs by some of the authors also yield a decoding algorithm for such lifted codes. We extend the framework to list decoding, by requiring the SOS solution to minimize a convex proxy for negative entropy. We show that this ensures a covering property for the SOS solution, and the “condition and round” approach used in several SOS algorithms can then be used to recover the required list of codewords.

We consider the problem of approximately solving constraint satisfaction problems with arity k > 2 (kCSPs) on instances satisfying certain expansion properties, when viewed as hypergraphs. Random instances of k-CSPs, which are also highly expanding, are well-known to be hard to approximate using known algorithmic techniques (and are widely believed to be hard to approximate in polynomial time). However, we show that this is not necessarily the case for instances where the hypergraph is a high-dimensional expander. We consider the spectral definition of highdimensional expansion used by Dinur and Kaufman [FOCS 2017] to construct certain primitives related to PCPs. They measure the expansion in terms of a parameter γ which is the analogue of the second singular value for expanding graphs. Extending the results by Barak, Raghavendra and Steurer [FOCS 2011] for 2-CSPs, we show that if an instance of MAX k-CSP over alphabet [q] is a high-dimensional expander with parameter γ, then it is possible to approximate the maximum fraction of satisfiable constraints up to an additive error ε using qO(k)· (k/ε)O(1)levels of the sum-of-squares SDP hierarchy, provided γ ≤ εO(1)· (1/(kq))O(k). Based on our analysis, we also suggest a notion of threshold-rank for hypergraphs, which can be used to extend the results for approximating 2-CSPs on low threshold-rank graphs. We show that if an instance of MAX k-CSP has threshold rank r for a threshold τ = (ε/k)O(1)· (1/q)O(k), then it is possible to approximately solve the instance up to additive error ε, using r · qO(k)· (k/ε)O(1)levels of the sum-of-squares hierarchy. As in the case of graphs, high-dimensional expanders (with sufficiently small γ) have threshold rank 1 according to our definition.

We study the problem of computing the p → q operator norm of a matrix A in ℝm×n, defined as ‖A‖p→q : = supx∊ℝn\{0} ‖Ax‖q/‖x‖p. This problem generalizes the spectral norm of a matrix (p = q = 2) and the Grothendieck problem (p = ∞, q = 1), and has been widely studied in various regimes. When p ≥ q, the problem exhibits a dichotomy: constant factor approximation algorithms are known if 2 is in [q, p], and the problem is hard to approximate within almost polynomial factors when 2 is not in [q,p]. For the case when 2 is in [q, p] we prove almost matching approximation and NP-hardness results. The regime when p < q, known as hypercontractive norms, is particularly significant for various applications but much less well understood. The case with p = 2 and q > 2 was studied by [Barak et. al., STOC’12] who gave sub-exponential algorithms for a promise version of the problem (which captures small-set expansion) and also proved hardness of approximation results based on the Exponential Time Hypothesis. However, no NP-hardness of approximation is known for these problems for any p < q. We prove the first NP-hardness result for approximating hypercontractive norms. We show that for any 1 < p < q < ∞ with 2 not in [p, q], ‖A‖p→q is hard to approximate within 2O(log1−∊ n) assuming NP is not contained in BPTIME(2logO(1) n)).

We consider the question of decoding Reed-Muller codes over beyond their list-decoding radius. Since, by definition, in this regime one cannot demand an efficient exact listdecoder, we seek an approximate decoder: Given a word F and radii r‘ > r > 0, the goal is to output a codeword within radius r’ of F, if there exists a codeword within distance r. As opposed to the list decoding problem, it suffices here to output any codeword with this property, since the list may be too large if r exceeds the list decoding radius. Prior to our work, such decoders were known for Reed-Muller codes of degree 2, due to works of Wolf and the second author [FOCS 2011]. In this work we make the first progress on this problem for the degree 3 where the list decoding radius is 1/8. We show that there is a constant and an efficient approximate decoder, that given query access to a function , such that F is within distance r = δ – ε from a cubic polynomial, runs in time polynomial in message length and outputs with high probability a cubic polynomial which is at distance at most r’ = 1/2 – ε‘ from F, where ε’ is a quasi polynomial function of ε.

We study the approximability of constraint satisfaction problems (CSPs) by linear programming (LP) relaxations. We show that for every CSP, the approximation obtained by a basic LP relaxation, is no weaker than the approximation obtained using relaxations given by $Ω\left(\frac{\log n}{\log \log n}\right)$ levels of the Sherali-Adams hierarchy on instances of size $n$. It was proved by Chan et al. [FOCS 2013] that any polynomial size LP extended formulation is no stronger than relaxations obtained by a super-constant levels of the Sherali-Adams hierarchy.. Combining this with our result also implies that any polynomial size LP extended formulation is no stronger than the basic LP. Using our techniques, we also simplify and strengthen the result by Khot et al. [STOC 2014] on (strong) approximation resistance for LPs. They provided a necessary and sufficient condition under which $Ω(\log \log n)$ levels of the Sherali-Adams hierarchy cannot achieve an approximation better than a random assignment. We simplify their proof and strengthen the bound to $Ω\left(\frac{\log n}{\log \log n}\right)$ levels.

We consider the following basic problem: given an n-variate degree-d homogeneous polynomial f with real coefficients, compute a unit vector x in R̂n that maximizes abs(f(x)). Besides its fundamental nature, this problem arises in diverse contexts ranging from tensor and operator norms to graph expansion to quantum information theory. The homogeneous degree-2 case is efficiently solvable as it corresponds to computing the spectral norm of an associated matrix, but the higher degree case is NP-hard. We give approximation algorithms for this problem that offer a trade-off between the approximation ratio and running time: in n̂O(q) time, we get an approximation within factor (O(n)/q)̂(d/2-1) for arbitrary polynomials, (O(n)/q)̂(d/4-1/2) for polynomials with non-negative coefficients, and (m /q)̂(1/2) for sparse polynomials with m monomials. The approximation guarantees are with respect to the optimum of the level-q sum-of-squares (SoS) SDP relaxation of the problem (though our algorithms do not rely on actually solving the SDP). Known polynomial time algorithms for this problem rely on “decoupling lemmas.” Such tools are not capable of offering a trade-off like our results as they blow up the number of variables by a factor equal to the degree. We develop new decoupling tools that are more efficient in the number of variables at the expense of less structure in the output polynomials. This enables us to harness the benefits of higher level SoS relaxations. Our decoupling methods also work with “folded polynomials,” which are polynomials with polynomials as coefficients. This allows us to exploit easy substructures (such as quadratics) by considering them as coefficients in our algorithms. We complement our algorithmic results with some polynomially large integrality gaps for d-levels of the SoS relaxation. For general polynomials this follows from known results for random polynomials, which yield a gap of Omega(n)̂(d/4-1/2). For polynomials with non-negative coefficients, we prove an Omega(n̂(1/6) /polylogs) gap for the degree-4 case, based on a novel distribution of 4-uniform hypergraphs. We establish an n̂Omega(d) gap for general degree-d, albeit for a slightly weaker (but still very natural) relaxation. Toward this, we give a method to lift a level-4 solution matrix M to a higher level solution, under a mild technical condition on M. From a structural perspective, our work yields worst-case convergence results on the performance of the sum-of-squareshierarchy for polynomial optimization. Despite the popularity of SoS in this context, such results were previously only known for the case of q = Omega(n).

We consider the problem of recovering a planted pseudorandom 3-coloring in expanding and low threshold-rank graphs. Alon and Kahale [SICOMP 1997] gave a spectral algorithm to recover the coloring for a random graph with a planted random 3-coloring. We show that their analysis can be adapted to work when coloring is pseudorandom i.e., all color classes are of equal size and the size of the intersection of the neighborhood of a random vertex with each color class has small variance. We also extend our results to partial colorings and low threshold-rank graphs to show the following: * For graphs on n vertices with threshold-rank r, for which there exists a 3-coloring that is eps-pseudorandom and properly colors the induced subgraph on (1-gamma)n vertices, we show how to recover the coloring for (1 - O(gamma + eps)) n vertices in time (rn)^{O(r)}. * For expanding graphs on n vertices, which admit a pseudorandom 3-coloring properly coloring all the vertices, we show how to recover such a coloring in polynomial time. Our results are obtained by combining the method of Alon and Kahale, with eigenspace enumeration methods used for solving constraint satisfaction problems on low threshold-rank graphs.

A boolean predicate is said to be strongly approximation resistant if, given a near-satisfiable instance of its maximum constraint satisfaction problem, it is hard to find an assignment such that the fraction of constraints satisfied deviates significantly from the expected fraction of constraints satisfied by a random assignment. A predicate which is not strongly approximation resistant is known as weakly approximable. We give a new method for proving the weak approximability of predicates, using a simple SDP relaxation, without designing and analyzing new rounding algorithms for each predicate. Instead, we use the recent characterization of strong approximation resistance by Khot et al. [STOC 2014], and show how to prove that for a given predicate, certain necessary conditions for strong resistance derived from their characterization, are violated. By their result, this implies the existence of a good rounding algorithm, proving weak approximability. We show how this method can be used to obtain simple proofs of (weak approximability analogues of) various known results on approximability, as well as new results on weak approximability of symmetric predicates.

In analogy with the regularity lemma of Szemerédi [Sze75], regularity lemmas for polynomials proved by Green and Tao [GT09] and by Kaufman and Lovett [KL08] show that one can modify a given collection of polynomials ℱ = {P1, …, Pm} into a new collection ℱ′ so that the polynomials in ℱ′ are “pseudorandom”. These lemmas have various applications, such as (special cases of) Reed-Muller testing and worst-case to average-case reductions for polynomials. However, the transformation from ℱ to ℱ′ in these works is not algorithmic. We define new notions of regularity for polynomials which, while being qualitatively equivalent to the above, also allow for efficient algorithms. Using the algorithmic regularity lemmas, we obtain an algorithmic version of the inverse theorem for Gowers norm (for bounded degree polynomials) over fields of high characteristic, by Green and Tao [GT09]. As an application, we show that if a polynomial P of degree d is within (normalized) Hamming distance of some unknown polynomial of degree k over a prime field (for k < d < | |), then there is an efficient algorithm for finding a degree-k polynomial Q, which is within distance of P, for some η depending on ε, This can be thought of as decoding the Reed-Muller code of order k beyond the list decoding radius, in the sense of finding one close codeword, when the received word P itself is a polynomial (of degree larger than k but smaller than | |). We also show an algorithmic inverse theorem for polynomials over fields of small characteristic and somewhat simplify the original proof by Tao and Ziegler [TZ12]. We also obtain an algorithmic version of the worstcase to average-case reductions by Kaufman and Lovett [KL08]. They show that if a polynomial of degree d can be weakly approximated by a polynomial of lower degree, then it can be computed exactly using a collection of polynomials of degree at most d − 1. We give an effcient algorithm to find this collection. Finally, our algorithmic regularity lemma over low characteristics can be used to effciently decompose low-degree polynomials over n (for any prime order field ) into polynomials P1, …, Pm: n → that satisfy prescribed degree bounds and for which P(x) = Λ(P1(x), …. Pm(x)) for a given m and Λ.

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For a predicate f: {-1, 1}k ↦ {0, 1} with ρ(f) = |f-1(1)|/2k, we call the predicate strongly approximation resistant if given a near-satisfiable instance of CSP(f), it is computationally hard to find an assignment such that the fraction of constraints satisfied is outside the range [ρ(f) - Ω(1), ρ(f) + Ω(1)].

We consider the complexity of LS+refutations of unsatisfiable instances of Constraint Satisfaction Problems (k-CSPs) when the underlying predicate supports a pairwise independent distribution on its satisfying assignments. This is the most general condition on the predicates under which the corresponding MAX k-CSP problem is known to be approximation resistant. We show that for random instances of such k-CSPs on n variables, even after Ω(n) rounds of the LS+hierarchy, the integrality gap remains equal to the approximation ratio achieved by a random assignment. In particular, this also shows that LS+refutations for such instances require rank Ω(n). We also show the stronger result that refutations for such instances in the static LS+proof system requires size exp(Ω(n)).

We construct a PCP based on the hyper-graph linearity test with 3 free queries. It has near-perfect completeness and soundness strictly less than 1/8. Such a PCP was known before only assuming the Unique Games Conjecture, albeit with soundness arbitrarily close to 1/16. At a technical level, our main contribution is constructing a new outer PCP which is "robust" against bounded degree polynomials, and showing that it can be composed with the hyper-graph linearity test with 3 free queries. We believe this outer PCP may be useful in obtaining the optimal query vs. soundness tradeoff for PCPs.

The Small-Set Expansion Hypothesis (Raghavendra, Steurer, STOC 2010) is a natural hardness assumption concerning the problem of approximating the edge expansion of small sets in graphs. This hardness assumption is closely connected to the Unique Games Conjecture (Khot, STOC 2002). In particular, the Small-Set Expansion Hypothesis implies the Unique Games Conjecture (Raghavendra, Steurer, STOC 2010). Our main result is that the Small-Set Expansion Hypothesis is in fact equivalent to a variant of the Unique Games Conjecture. More precisely, the hypothesis is equivalent to the Unique Games Conjecture restricted to instance with a fairly mild condition on the expansion of small sets. Alongside, we obtain the first strong hardness of approximation results for the Balanced Separator and Minimum Linear Arrangement problems. Before, no such hardness was known for these problems even assuming the Unique Games Conjecture. These results not only establish the Small-Set Expansion Hypothesis as a natural unifying hypothesis that implies the Unique Games Conjecture, all its consequences and, in addition, hardness results for other problems like Balanced Separator and Minimum Linear Arrangement, but our results also show that the Small-Set Expansion Hypothesis problem lies at the combinatorial heart of the Unique Games Conjecture. The key technical ingredient is a new way of exploiting the structure of the Unique Games instances obtained from the Small-Set Expansion Hypothesis via (Raghavendra, Steurer, 2010). This additional structure allows us to modify standard reductions in a way that essentially destroys their local-gadget nature. Using this modification, we can argue about the expansion in the graphs produced by the reduction without relying on expansion properties of the underlying Unique Games instance (which would be impossible for a local-gadget reduction).

We initiate a principled study of graph densification. Given a graph G the goal of graph densification is to come up with another graph H that has significantly more edges than G but nevertheless approximates G well with respect to some set of test functions. In this paper we focus on the case of cut and spectral approximations.

Decomposition theorems in classical Fourier analysis enable us to express a bounded function in terms of few linear phases with large Fourier coefficients plus a part that is pseudorandom with respect to linear phases. The Goldreich--Levin algorithm [O. Goldreich and L. Levin, “A Hard-Core Predicate for All One-Way Functions," in Proceedings of the 21st Annual ACM Symposium on Theory of Computing, 1989, pp. 25--32] can be viewed as an algorithmic analogue of such a decomposition as it gives a way to efficiently find the linear phases associated with large Fourier coefficients. In the study of “quadratic Fourier analysis,” higher-degree analogues of such decompositions have been developed in which the pseudorandomness property is stronger but the structured part is correspondingly weaker. For example, it has been shown that it is possible to express a bounded function as a sum of a few quadratic phases plus a part that is small in the $U^3$ norm, defined by Gowers for the purpose of counting arithmetic progressions of length 4. We give a polynomial time algorithm for computing such a decomposition. A key part of the algorithm is a local self-correction procedure for Reed--Muller codes of order 2 (over $\F_2^n$) for a function at distance $1/2-\e$ from a codeword. Given a function $f:\F_2^n \to \{-1,1\}$ at fractional Hamming distance $1/2-\e$ from a quadratic phase (which is a codeword of the Reed--Muller code of order 2), we give an algorithm that runs in time polynomial in $n$ and finds a codeword at distance at most $1/2-\eta$ for $\eta = \eta(\e)$. This is an algorithmic analogue of Samorodnitsky's result [A. Samorodnitsky, “Low-Degree Tests at Large Distances,” in Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 2007, pp. 506--515], which gave a tester for the above problem. To our knowledge, it represents the first instance of a correction procedure for any class of codes, beyond the list-decoding radius. For this purpose, we give algorithmic versions of results from additive combinatorics used in Samorodnitsky's proof and a refined version of the inverse theorem for the Gowers $U^3$ norm over $\F_2^n$.

The subspace approximation problem Subspace(k, p) asks for a k dimensional linear subspace that fits a given set of m points in ℝn optimally. The error for fitting is a generalization of the least squares fit and uses the ℓp norm of the distances (ℓ2 distances) of the points from the subspace, e.g., p = ∞ means minimizing the ℓ2 distance of the farthest point from the subspace. Previous work on subspace approximation considers either the case of small or constant k and p [27, 11, 14] or the case of p = ∞ [16, 8, 17, 7, 24, 23, 29]. In this paper, we study the algorithms and hardness for Subspace(k, p) in the natural range 1 ≤ k ≤ n and 2 ≤ p ≤ ∞. Our results are as follows. Extending the convex relaxation and rounding techniques of Varadarajan, Venkatesh, Ye and Zhang [29], we give a polynomial time approximation algorithm for Subspace(k, p), for any k and any p ≥ 2, with an approximation guarantee of roughly , where is the pth moment of a standard normal variable. This improves to γp for k = n − 1. We exhibit a simple integrality gap (or “rank gap”) instance for our convex relaxation giving a gap of γp(1 − ε), for any constant ε > 0. We show that, assuming the Unique Games Conjecture, the subspace approximation problem is hard to approximate within a factor better than γp(1 − ε), for any constant ε > 0. Our hardness reduction involves a dictatorship test which is somewhat different from “long code” based tests used in reductions from Unique Games, and seems better suited for problems of a continuous nature.

In a beautiful result, Raghavendra established optimal Unique Games Conjecture (UGC)-based inapproximability for a large class of constraint satisfaction problems (CSPs). In the class of CSPs he considers, of which Maximum Cut is a prominent example, the goal is to find an assignment which maximizes a weighted fraction of constraints satisfied. He gave a generic semi-definite program (SDP) for this class of problems and showed how the approximability of each problem is determined by the corresponding SDP (upto an arbitrarily small additive error) assuming the UGC. He noted that his techniques do no apply to CSPs with strict constraints (all of which must be satisfied) such as Vertex Cover. In this paper we address the approximability of these strict-CSPs. In the class of CSPs we consider, one is given a set of constraints over a set of variables, and a cost function over the assignments, the goal is to find an assignment to the variables of minimum cost which satisfies all the constraints. We present a generic linear program (LP) for a large class of strict-CSPs and give a systematic way to convert integrality gaps for this LP into UGC-based inapproximability results. Some important problems whose approximability our framework captures are Vertex Cover, Hypergraph Vertex Cover, k-partite-Hypergraph Vertex Cover, Independent Set and other covering and packing problems over q-ary alphabets, and a scheduling problem. For the covering and packing problems, which occur quite commonly in practice as well, we provide a matching rounding algorithm, thus settling their approximability upto an arbitrarily small additive error.

We show that every bounded function g: {0,1}nrarr [0,1] admits an efficiently computable "simulator" function h: {0,1}nrarr [0,1] such that every fixed polynomial size circuit has approximately the same correlation with g as with h. If g describes (up to scaling) a high min-entropy distribution D, then h can be used to efficiently sample a distribution D' of the same min-entropy that is indistinguishable from D by circuits of fixed polynomial size. We state and prove our result in a more abstract setting, in which we allow arbitrary finite domains instead of {0,1}n, and arbitrary families of distinguishers, instead of fixed polynomial size circuits. Our result implies (a) the weak Szemeredi regularity Lemma of Frieze and Kannan (b) a constructive version of the dense model theorem of Green, Tao and Ziegler with better quantitative parameters (polynomial rather than exponential in the distinguishing probability), and (c) the Impagliazzo hardcore set Lemma. It appears to be the general result underlying the known connections between "regularity" results in graph theory, "decomposition" results in additive combinatorics, and the hardcore Lemma in complexity theory. We present two proofs of our result, one in the spirit of Nisan's proof of the hardcore Lemma via duality of linear programming, and one similar to Impagliazzo's "boosting" proof. A third proof by iterative partitioning, which gives the complexity of the sampler to be exponential in the distinguishing probability, is also implicit in the Green-Tao-Ziegler proofs of the dense model theorem.

We study integrality gaps for SDP relaxations of constraint satisfaction problems, in the hierarchy of SDPs defined by Lasserre. Schoenebeck [23] recently showed the first integrality gaps for these problems, showing that for MAX k-XOR, the ratio of the SDP optimum to the integer optimum may be as large as 2 even after Ω(n) rounds of the Lasserre hierarchy. We show that for the general MAX k-CSP problem, this ratio can be as large as 2k/2k - ε when the alphabet is binary and qk/q(q-1)k - ε when the alphabet size a prime q, even after Ω(n) rounds of the Lasserre hierarchy. We also explore how to translate gaps for CSP into integrality gaps for other problems using reductions, and establish SDP gaps for Maximum Independent Set, Approximate Graph Coloring, Chromatic Number and Minimum Vertex Cover. For Independent Set and Chromatic Number, we show integrality gaps of n/2O(√(log n log log n)) even after 2Ω(√(log n log log n)) rounds. In case of Approximate Graph Coloring, for every constant l, we construct graphs with chromatic number Ω(2l/2/l2), which admit a vector l-coloring for the SDP obtained by Ω(n) rounds. For Vertex Cover, we show an integrality gap of 1.36 for Ω(nδ) rounds, for a small constant δ. The results for CSPs provide the first examples of Ω(n) round integrality gaps matching hardness results known only under the Unique Games Conjecture. This and some additional properties of the integrality gap instance, allow for gaps for in case of Independent Set and Chromatic Number which are stronger than the NP-hardness results known even under the Unique Games Conjecture.

A theorem of Green, Tao, and Ziegler can be stated (roughly) as follows: ifR is a pseudorandom set, and D is a dense subset of R, then D may be modeled by a set M that is dense in the entire domain such that D and M are indistinguishable. (The precise statement refers to"measures" or distributions rather than sets.) The proof of this theorem is very general, and it applies to notions of pseudo-randomness and indistinguishability defined in terms of any family of distinguishers with some mild closure properties. The proof proceeds via iterative partitioning and an energy increment argument, in the spirit of the proof of the weak Szemeredi regularity lemma. The "reduction" involved in the proof has exponential complexity in the distinguishing probability. We present a new proof inspired by Nisan's proof of Impagliazzo's hardcore set theorem. The reduction in our proof has polynomial complexity in the distinguishing probability and provides a new characterization of the notion of "pseudoentropy" of a distribution. A proof similar to ours has also been independently discovered by Gowers [2]. We also follow the connection between the two theorems and obtain a new proof of Impagliazzo's hardcore set theorem via iterative partitioning and energy increment. While our reduction has exponential complexity in some parameters, it has the advantage that the hardcore set is efficiently recognizable.

We present an efficient algorithm to find a good solution to the Unique Games problem when the constraint graph is an expander.

We study semidefinite programming relaxations of Vertex Cover arising from repeated applications of the LS+ "lift-and-project" method of Lovasz and Schrijver starting from the standard linear programming relaxation. Goemans and Kleinberg prove that after one round of LS+ the integrality gap remains arbitrarily close to 2. Charikar proves an integrality gap of 2, later strengthened by Hatami, Magen, and Markakis, for stronger relaxations that are, however, incomparable with two rounds of LS+. Subsequent work by Georgiou, Magen, Pitassi, and Tourlakis shows that the integrality gap remains 2 -epsiv after Omega (radiclog n-log log n ) rounds [?]. We prove that the integrality gap remains at least 7/6 - epsiv after cepsivn rounds, where n is the number of vertices and cepsiv> 0 is a constant that depends only on epsiv.

We study linear programming relaxations of Vertex Cover and Max Cutarising from repeated applications of the "lift-and-project" method of Lovasz and Schrijver starting from the standard linear programming relaxation.