Correlation clustering is a well-studied problem, first proposed by Bansal, Blum, and Chawla [Mach. Learn. '04]. The input is an unweighted, undirected graph. The problem is to cluster the vertices so as to minimize the number of edges between vertices in different clusters and missing edges between vertices inside the same cluster. This problem has a wide application in data mining and machine learning. We introduce a general framework that transforms existing static correlation clustering algorithms into fully-dynamic ones that work against an adaptive adversary. We show how to apply our framework to known efficient correlation clustering algorithms, starting from the classic 3-approximate Pivot algorithm from Ailon, Charikar and Newman [JACM'08]. Applied to the most recent sublinear $1.485$-approximation algorithm from Cao, Cohen-Addad, Lee, Li, Lolck, Newman, Thorup, Vogl, Yan and Zhang [STOC'25], we get a $1.485$-approximation fully-dynamic algorithm that works with worst-case constant update time. The original static algorithm gets its approximation factor with constant probability, and we get the same against an adaptive adversary in the sense that for any given update step, not known to our algorithm, our solution is a $1.485$-approximation with constant probability when we reach this update. Most of previous dynamic algorithms, including the celebrated result from Behnezhad, Charikar, Ma and Tan [FOCS'19], had approximation factors around $3$ in expectation, and they could only handle an oblivious adversary. A recent algorithm by Braverman, Dharangutte, Pai, Shah, and Wang [AISTATS'25] could handle an adaptive adversary, but it has a large unspecified constant approximation ratio. This contrasts with our general transformation, which works with all the best approximation factors known for the static case.

We present a polynomial-time (4+є)-approximation algorithm for (high-dimensional) Euclidean k-Means. This substantially improves on the current-best 5.83-approximation in [Charikar, Cohen-Addad, Gao, Grandoni, Lee, Van Wijland - FOCS’25] (that also works for the metric case). The mentioned algorithm by Charikar et al. critically exploits a greedy Lagrangian Multiplier Preserving (LMP) approximation for Facility Location with squared metric distances, that adapts the classical greedy algorithm with dual-fitting analysis for Metric Facility Location in [Jain, Mahdian, Markakis, Saberi, Vazirani - J.ACM’03]. The authors then turn it into an approximation algorithm for (Metric) k-Means, at the cost on an extra factor 1+є, by exploiting the framework introduced in [Cohen-Addad, Grandoni, Lee, Schwiegelshohn, Svensson - STOC’25] for k-Median. Our main contribution is a greedy LMP 4-approximation for Facility Location with squared Euclidean distances. Differently from Charikar et al., our algorithm sometimes decreases the dual variables, a quite uncommon feature for dual-based algorithms. This is critical in our dual-fitting analysis in order to exploit the specific properties of Euclidean metrics. For the (4+є)-approximation for k-Means, we extend the framework by Cohen-Addad et al. by overcoming substantial technical challenges posed by decreased dual values.

In a linear combinatorial optimization problem, we are given a family F ⊆ 2U of feasible subsets of a ground set U of n elements, and our goal is to find S* = argminS ∈ F ⟨ w,1S ⟩. Traditionally, we are either given the weight vector up-front, or else we are given a value oracle which allows us to evaluate w(S) := ⟨ w, 1S ⟩ for any S ∈ F. We consider the weaker and more robust comparison oracle, which for any two feasible sets S, T ∈ F, reveals only if w(S) is less than/equal to/greater than w(T). We ask: When can we find the optimal feasible set S* = argminS ∈ F w(S) using a small number of comparison queries? If so, when can we do this efficiently?

This paper studies complete k-Constraint Satisfaction Problems (CSPs), where an n-variable instance has exactly one nontrivial constraint for each subset of k variables, i.e., it has binom(n,k) constraints. A recent work started a systematic study of complete k-CSPs [Anand, Lee, Sharma, SODA'25], and showed a quasi-polynomial time algorithm that decides if there is an assignment satisfying all the constraints of any complete Boolean-alphabet k-CSP, algorithmically separating complete instances from dense instances. The tractability of this decision problem is necessary for any nontrivial (multiplicative) approximation for the minimization version, whose goal is to minimize the number of violated constraints. The same paper raised the question of whether it is possible to obtain nontrivial approximation algorithms for complete Min-k-CSPs with k ≥ 3. In this work, we make progress in this direction and show a quasi-polynomial time polylog(n)-approximation to Min-NAE-3-SAT on complete instances, which asks to minimize the number of 3-clauses where all the three literals equal the same bit. To the best of our knowledge, this is the first known example of a CSP whose decision version is NP-Hard in general (and dense) instances while admitting a polylog(n)-approximation in complete instances. Our algorithm presents a new iterative framework for rounding a solution from the Sherali-Adams hierarchy, where each iteration interleaves the two well-known rounding tools: the conditioning procedure, in order to "almost fix" many variables, and the thresholding procedure, in order to "completely fix" them. Finally, we improve the running time of the decision algorithms of Anand, Lee, and Sharma and show a simple algorithm that decides any complete Boolean-alphabet k-CSP in polynomial time.

The 𝓁₂² min-sum k-clustering problem is to partition an input set into clusters C_1,…,C_k to minimize ∑_{i=1}^k ∑_{p,q ∈ C_i} ‖p-q‖₂². Although 𝓁₂² min-sum k-clustering is NP-hard, it is not known whether it is NP-hard to approximate 𝓁₂² min-sum k-clustering beyond a certain factor. In this paper, we give the first hardness-of-approximation result for the 𝓁₂² min-sum k-clustering problem. We show that it is NP-hard to approximate the objective to a factor better than 1.056 and moreover, assuming a balanced variant of the Johnson Coverage Hypothesis, it is NP-hard to approximate the objective to a factor better than 1.327. We then complement our hardness result by giving a fast PTAS for 𝓁₂² min-sum k-clustering. Specifically, our algorithm runs in time O(n^{1+o(1)}d⋅ 2^{(k/ε)^O(1)}), which is the first nearly linear time algorithm for this problem. We also consider a learning-augmented setting, where the algorithm has access to an oracle that outputs a label i ∈ [k] for input point, thereby implicitly partitioning the input dataset into k clusters that induce an approximately optimal solution, up to some amount of adversarial error α ∈ [0,1/2). We give a polynomial-time algorithm that outputs a (1+γα)/(1-α)²-approximation to 𝓁₂² min-sum k-clustering, for a fixed constant γ > 0.

Finding sparse vectors is a fundamental problem that arises in several contexts including codes, subspaces, and lattices. In this work, we prove strong inapproximability results for all these variants using a novel approach that even bypasses the PCP theorem. Our main result is that it is NP-hard (under randomized reductions) to approximate the sparsest vector in a real subspace within any constant factor; the gap can be further amplified using tensoring. Our reduction has the property that there is a Boolean solution in the completeness case. As a corollary, this immediately recovers the state-of-the-art inapproximability factors for the shortest vector problem (SVP) on lattices. Our proof extends the range of $\mathbf{l}_{\_} \mathbf{p}$ (quasi) norms for which hardness was previously known, from ‘p at least one’ to ‘p at least zero’, answering a question raised by (Khot, JACM 2005).Previous hardness results for SVP, and the related minimum distance problem (MDP) for error-correcting codes, all use lattice/coding gadgets that have an abundance of codewords in a ball of radius smaller than the minimum distance. In contrast, our reduction only needs many codewords in a ball of radius slightly larger than the minimum distance. This enables an easy derandomization of our reduction for finite fields, giving a new elementary proof of deterministic hardness for MDP. We believe this weaker density requirement might offer a promising approach to showing deterministic hardness of SVP, a long elusive goal. The key technical ingredient underlying our result for real subspaces is a proof that in the kernel of a random Rademacher matrix, the support of any two linearly independent vectors have very little overlap.A broader motivation behind this work is the development of inapproximability techniques for problems over the reals. Analytic variants of sparsest vector have connections to small set expansion, quantum separability and polynomial maximization over convex sets, all of which appear to be out of reach of current PCP techniques. We hope that the approach we develop could enable progress on some of these problems.

Clustering is a basic task in data analysis and machine learning, and the optimization of clustering objectives are well-studied optimization problems; amongst these, the k Means objective is arguably the most well known. Given a collection of points in a metric space, the goal is to partition them into k clusters, each with an associated center, so as to minimize the sum of squared distances of points to their cluster centers. In this paper, we present a polynomial-time $3+2 \sqrt{2}+\varepsilon{\lt}5.83$-approximation algorithm for k-Means in general metrics. This substantially improves on the current-best $(9+\varepsilon)$-approximation in [Ahmadian, Norouzi-Fard, Svensson, Ward - FOCS’17, SICOMP’20], and even slightly improves on the 5.92-approximation in [Cohen-Addad, Esfandiari, Mirrokni, Narayanan - STOC’22] for the Euclidean special case. A natural approach for k-Means is to leverage Lagrangian Multiplier Preserving (LMP) approximations for the facility location problem. The previous best results for k-Means build upon an adaptation of an LMP 3-approximation for facility location with metric connection costs in [Jain, Vazirani J.ACM’01] based on a primal-dual method, rather than on the improved LMP greedy 2-approximation for the same problem in [Jain, Mahdian, Markakis, Saberi, Vazirani - J.ACM’03]. The barrier to using the improved LMP algorithm was that no adaptation of this algorithm and its analysis to the case of squared metric connection costs was known (since squared distances violate triangle inequality). Our main contribution is overcoming this barrier by providing such an adaptation. This new LMP approximation algorithm is then combined with the framework recently introduced in [Cohen-Addad, Grandoni, Lee, Schwiegelshohn, Svensson - STOC’25] for the related (metric) k Median problem.

Given a directed graph G with n vertices and m edges, a parameter k and two disjoint subsets S,T ⊆ V(G), we show that the number of all-subsets important separators, which is the number of A-B important vertex separators of size at most k over all A ⊆ S and B ⊆ T, is at most β(|S|, |T|, k) = 4^k binom(|S|, ≤ k) binom(|T|, ≤ 2k), where binom(x, ≤ c) = ∑_{i = 1}^c binom(x,i), and that they can be enumerated in time 𝒪(β(|S|,|T|,k)k²(m+n)). This is a generalization of the folklore result stating that the number of A-B important separators for two fixed sets A and B is at most 4^k (first implicitly shown by Chen, Liu and Lu Algorithmica '09). From this result, we obtain the following applications: 1) We give a construction for detection sets and sample sets in directed graphs, generalizing the results of Kleinberg (Internet Mathematics' 03) and Feige and Mahdian (STOC' 06) to directed graphs. 2) Via our new sample sets, we give the first FPT algorithm for finding balanced separators in directed graphs parameterized by k, the size of the separator. Our algorithm runs in time 2^{𝒪(k)} ⋅ (m + n). 3) Additionally, we show a 𝒪(√{log k}) approximation algorithm for finding balanced separators in directed graphs in polynomial time. This improves the best known approximation guarantee of 𝒪(√{log n}) and matches the known guarantee in undirected graphs by Feige, Hajiaghayi and Lee (SICOMP' 08). 4) Finally, using our algorithm for listing all-subsets important separators, we give a deterministic construction of vertex cut sparsifiers in directed graphs when we are interested in preserving min-cuts of size upto c between bipartitions of the terminal set. Our algorithm constructs a sparsifier of size 𝒪(binom(t, ≤ 3c)2^{𝒪(c)}) and runs in time 𝒪(binom(t, ≤ 3c) 2^{𝒪(c)}(m + n)), where t is the number of terminals, and the sparsifier additionally preserves the set of important separators of size at most c between bipartitions of the terminals.

Recent years have seen great progress in the approximability of fundamental clustering and facility location problems on high-dimensional Euclidean spaces, including $k$-Means and $k$-Median. While they admit strictly better approximation ratios than their general metric versions, their approximation ratios are still higher than the hardness ratios for general metrics, leaving the possibility that the ultimate optimal approximation ratios will be the same between Euclidean and general metrics. Moreover, such an improved algorithm for Euclidean spaces is not known for Uncapaciated Facility Location (UFL), another fundamental problem in the area. In this paper, we prove that for any $γ\geq 1.6774$ there exists $\varepsilon > 0$ such that Euclidean UFL admits a $(γ, 1 + 2e^{-γ} - \varepsilon)$-bifactor approximation algorithm, improving the result of Byrka and Aardal. Together with the $(γ, 1 + 2e^{-γ})$ NP-hardness in general metrics, it shows the first separation between general and Euclidean metrics for the aforementioned basic problems. We also present an $(α_{Li} - \varepsilon)$-(unifactor) approximation algorithm for UFL for some $\varepsilon > 0$ in Euclidean spaces, where $α_{Li} \approx 1.488$ is the best-known approximation ratio for UFL by Li.

In this paper, we study the parameterized complexity of local search, whose goal is to find a good nearby solution from the given current solution. Formally, given an optimization problem where the goal is to find the largest feasible subset S of a universe U, the new input consists of a current solution P (not necessarily feasible) as well as an ordinary input for the problem. Given the existence of a feasible solution S^*, the goal is to find a feasible solution as good as S^* in parameterized time f(k)⋅n^O(1), where k denotes the distance |PΔ S^*|. This model generalizes numerous classical parameterized optimization problems whose parameter k is the minimum number of elements removed from U to make it feasible, which corresponds to the case P = U. We apply this model to widely studied Constraint Satisfaction Problems (CSPs), where U is the set of constraints, and a subset U' of constraints is feasible if there is an assignment to the variables satisfying all constraints in U'. We give a complete characterization of the parameterized complexity of all boolean-alphabet symmetric CSPs, where the predicate’s acceptance depends on the number of true literals.

Correlation Clustering (CC) is a foundational problem in unsupervised learning that models binary similarity relations using labeled graphs. While classical CC has been well studied, many real-world applications involve more nuanced relationships—either multi-class categorical interactions or varying confidence levels in edge labels. To address these, two natural generalizations have been proposed: Chromatic Correlation Clustering (CCC), which assigns semantic colors to edge labels, and pseudometric-weighted CC, which allows edge weights satisfying the triangle inequality. In this paper, we develop improved approximation algorithms for both settings. Our approach leverages LP-based pivoting techniques combined with problem-specific rounding functions. For the pseudometric-weighted correlation clustering problem, we present a tight $\frac{10}{3}$-approximation algorithm, matching the best possible bound achievable within the framework of standard LP relaxation combined with specialized rounding. For the Chromatic Correlation Clustering (CCC) problem, we improve the approximation ratio from the previous best of $2.5$ to $2.15$, and we establish a lower bound of $2.11$ within the same analytical framework, highlighting the near-optimality of our result.

Unbreakable decomposition, introduced by [CLP+19, CKL+20], has proven to be one of the most powerful tools for parameterized graph cut problems in recent years. Unfortunately, all known constructions require at least Ωk (mn2) time, given an undirected graph with n vertices, m edges, and cut-size parameter k. In this work, we show the first close-to-linear time parameterized algorithm that computes an unbreakable decomposition. More precisely, for any 0 < ∈ ≤ 1, our algorithm runs in time and computes a (O (k/∈ ),k ) unbreakable tree decomposition of the input graph, where each bag has adhesion at most O (k/∈ ).

Given a fixed arity k ≥ 2, MlN-k-CSP on complete instances is the problem whose input consists of a set of n variables V and one (nontrivial) constraint for every k-subset of variables (so there are constraints), and the goal is to find an assignment that minimizes the number of unsatisfied constraints. Unlike Max-k-CSP that admits a PTAS on more general dense or expanding instances, the approximability of Min-k-CSP has not been well understood. Moreover, for some CSPs including Min-k-SAT, there is an approximation-preserving reduction from general instances to dense and expanding instances, leaving complete instances as a unique family that may admit new algorithmic techniques.

Correlation Clustering is a fundamental and widely-studied problem in unsupervised learning and data mining. The input is a graph and the goal is to construct a clustering minimizing the number of inter-cluster edges plus the number of missing intra-cluster edges. CCL+24 introduced the cluster LP for Correlation Clustering, which they argued captures the problem much more succinctly than previous linear programming formulations. However, the cluster LP has exponential size, with a variable for every possible set of vertices in the input graph. Nevertheless, CCL+24 showed how to find a feasible solution for the cluster LP in time $O(n^{\text{poly}(1/ε)})$ with objective value at most $(1+ε)$ times the value of an optimal solution for the respective Correlation Clustering instance. Furthermore, they showed how to round a solution to the cluster LP, yielding a $(1.485+ε)$-approximation algorithm for the Correlation Clustering problem. The main technical result of this paper is a new approach to find a feasible solution for the cluster LP with objective value at most $(1+ε)$ of the optimum in time $\widetilde O(2^{\text{poly}(1/ε)} n)$, where $n$ is the number of vertices in the graph. We also show how to implement the rounding within the same time bounds, thus achieving a fast $(1.485+ε)$-approximation algorithm for the Correlation Clustering problem. This bridges the gap between state-of-the-art methods for approximating Correlation Clustering and the recent focus on fast algorithms.

In the classical NP-hard (metric) k-median problem, we are given a set of n clients and centers with metric distances between them, along with an integer parameter k ≥ 1. The objective is to select a subset of k open centers that minimizes the total distance from each client to its closest open center. In their seminal work, Jain, Mahdian, Markakis, Saberi, and Vazirani presented the Greedy algorithm for facility location, which implies a 2-approximation algorithm for k-median that opens k centers in expectation. Since then, substantial research has aimed at narrowing the gap between their algorithm and the best achievable approximation by an algorithm guaranteed to open exactly k centers, as required in the k-median problem. During the last decade, all improvements have been achieved by leveraging their algorithm (or a small improvement thereof), followed by a second step called bi-point rounding, which inherently adds an additional factor to the approximation guarantee. Our main result closes this gap: for any > 0, we present a (2+)-approximation algorithm for the k-median problem, improving the previous best-known approximation factor of 2.613. Our approach builds on a combination of two key algorithms. First, we present a non-trivial modification of the Greedy algorithm that operates with only O(logn/2) adaptive phases. Through a novel walk-between-solutions approach, this enables us to construct a (2+)-approximation algorithm for k-median that consistently opens at most k + O(logn/2) centers: via known results, this already implies a (2+)-approximation algorithm that runs in quasi-polynomial time. Second, we develop a novel (2+)-approximation algorithm tailored for stable instances, where removing any center from an optimal solution increases the cost by at least an ω(3/logn) fraction. Achieving this involves several ideas, including a sampling approach inspired by the k-means++ algorithm and a reduction to submodular optimization subject to a partition matroid. This allows us to convert the previous result into a polynomial time algorithm that opens exactly k centers while maintaining the (2+)-approximation guarantee.

For any 𝜀 > 0, we prove that 𝑘-Dimensional Matching is hard to approximate within a factor of 𝑘/(12+𝜀) for large 𝑘 unless NP ⊆ BPP. Listed in Karp’s 21 NP-complete problems, 𝑘-Dimensional Matching is a benchmark computational complexity problem which we find as a special case of many constrained optimization problems over independence systems including: 𝑘-Set Packing, 𝑘-Matroid Intersection, and Matroid 𝑘-Parity. For all the aforementioned problems, the best-known lower bound was a Ω(𝑘/log(𝑘))-hardness by Hazan, Safra, and Schwartz. In contrast, state-of-the-art algorithms achieve an approximation of 𝑂(𝑘). Our result narrows down this gap to a constant and thus provides a rationale for the observed algorithmic difficulties. The crux of our result hinges on a novel approximation preserving gadget from 𝑅-degree bounded 𝑘-CSPs over alphabet size 𝑅 to 𝑘𝑅-Dimensional Matching. Along the way, we prove that 𝑅-degree bounded 𝑘-CSPs over alphabet size 𝑅 are hard to approximate within a factor Ω𝑘 (𝑅) using known randomised sparsification methods for CSPs.

The Weighted F - Vertex Deletion for a class F of graphs asks, weighted graph G , for a minimum weight vertex set S such that G − S ∈ F . The case when F is minor-closed and excludes some graph as a minor has received particular attention but a constant-factor approximation remained elusive for Weighted F - Vertex Deletion . Only three cases of minor-closed F are known to admit constant-factor approximations, namely Vertex Cover , Feedback Vertex Set and Diamond Hitting Set . We study the problem for the class F of θ c -minor-free graphs, under the equivalent setting of the Weighted c -Bond Cover problem, and present a constant-factor approximation algorithm using the primal-dual method. Besides making an important step in the quest of (dis)proving a constant-factor approximation for Weighted F - Vertex Deletion , our result may be useful as a template for algorithms for other minor-closed families.

Abstract. Given [Formula: see text] recording pairwise distances, the Metric Violation Distance problem asks to compute the [Formula: see text] distance between [Formula: see text] and the metric cone; i.e., modify the minimum number of entries of [Formula: see text] to make it a metric. Due to its large number of applications in various data analysis and optimization tasks, this problem has been actively studied recently. We present an [Formula: see text]-approximation algorithm for Metric Violation Distance, exponentially improving the previous best approximation ratio of [Formula: see text] of Fan, Raichel, and Van Buskirk [ SODA, 2018]. Furthermore, a major strength of our algorithm is its simplicity and running time. We also study the related problem of Ultrametric Violation Distance, where the goal is to compute the [Formula: see text] distance to the cone of ultrametrics, and achieve a constant factor approximation algorithm. The Ultrametric Violation Distance problem can be regarded as an extension of the problem of fitting ultrametrics studied by Ailon and Charikar [ SIAM J. Comput., 2011] and by Cohen-Addad, Das, Kipouridis, Parotsidis, and Thorup [ FOCS, 2021] from [Formula: see text] norm to [Formula: see text] norm. We show that this problem can be favorably interpreted as an instance of Correlation Clustering with an additional hierarchical structure, which we solve using a new [Formula: see text]-approximation algorithm for correlation clustering that has the structural property that it outputs a refinement of the optimum clusters. An algorithm satisfying such a property can be considered of independent interest. We also provide an [Formula: see text]-approximation algorithm for a weighted version of Ultrametric Violation Distance. Finally, we investigate the complementary version of these problems where one aims at choosing a maximum number of entries of [Formula: see text] forming an (ultra)metric. In stark contrast to the minimization versions, we prove that these maximization versions are hard to approximate within any constant factor assuming the Unique Games Conjecture.

This paper studies $k$-claw-free graphs, exploring the connection between an extremal combinatorics question and the power of a convex program in approximating the maximum-weight independent set in this graph class. For the extremal question, we consider the notion, that we call \textit{conditional $χ$-boundedness} of a graph: Given a graph $G$ that is assumed to contain an independent set of a certain (constant) size, we are interested in upper bounding the chromatic number in terms of the clique number of $G$. This question, besides being interesting on its own, has algorithmic implications (which have been relatively neglected in the literature) on the performance of SDP relaxations in estimating the value of maximum-weight independent set. For $k=3$, Chudnovsky and Seymour (JCTB 2010) prove that any $3$-claw-free graph $G$ with an independent set of size three must satisfy $χ(G) \leq 2 ω(G)$. Their result implies a factor $2$-estimation algorithm for the maximum weight independent set via an SDP relaxation (providing the first non-trivial result for maximum-weight independent set in such graphs via a convex relaxation). An obvious open question is whether a similar conditional $χ$-boundedness phenomenon holds for any $k$-claw-free graph. Our main result answers this question negatively. We further present some evidence that our construction could be useful in studying more broadly the power of convex relaxations in the context of approximating maximum weight independent set in $k$-claw free graphs. In particular, we prove a lower bound on families of convex programs that are stronger than known convex relaxations used algorithmically in this context.

Parameterized Inapproximability Hypothesis (PIH) is a central question in the field of parameterized complexity. PIH asserts that given as input a 2-CSP on $k$ variables and alphabet size $n$, it is W[1]-hard parameterized by $k$ to distinguish if the input is perfectly satisfiable or if every assignment to the input violates 1% of the constraints. An important implication of PIH is that it yields the tight parameterized inapproximability of the $k$-maxcoverage problem. In the $k$-maxcoverage problem, we are given as input a set system, a threshold $τ>0$, and a parameter $k$ and the goal is to determine if there exist $k$ sets in the input whose union is at least $τ$ fraction of the entire universe. PIH is known to imply that it is W[1]-hard parameterized by $k$ to distinguish if there are $k$ input sets whose union is at least $τ$ fraction of the universe or if the union of every $k$ input sets is not much larger than $τ\cdot (1-\frac{1}{e})$ fraction of the universe. In this work we present a gap preserving FPT reduction (in the reverse direction) from the $k$-maxcoverage problem to the aforementioned 2-CSP problem, thus showing that the assertion that approximating the $k$-maxcoverage problem to some constant factor is W[1]-hard implies PIH. In addition, we present a gap preserving FPT reduction from the $k$-median problem (in general metrics) to the $k$-maxcoverage problem, further highlighting the power of gap preserving FPT reductions over classical gap preserving polynomial time reductions.

Recovering the underlying clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query $S \subset U$, $|S|=2$, the oracle returns "yes" if the points are in the same cluster and "no" otherwise. We study a natural generalization of this problem to subset queries for $|S|>2$, where the oracle returns the number of clusters intersecting $S$. Our aim is to determine the minimum number of queries needed for exactly recovering an arbitrary $k$-clustering. We focus on non-adaptive schemes, where all the queries are asked in one round, thus allowing for the querying process to be parallelized, which is a highly desirable property. For adaptive algorithms with pair-wise queries, the complexity is known to be $\Theta(nk)$, where $k$ is the number of clusters. In contrast, non-adaptive pair-wise query algorithms are extremely limited: even for $k=3$, such algorithms require $\Omega(n^2)$ queries, which matches the trivial $O(n^2)$ upper bound attained by querying every pair of points. Allowing for subset queries of unbounded size, $O(n)$ queries is possible with an adaptive scheme. However, the realm of non-adaptive algorithms remains completely unknown. Is it possible to attain algorithms that are non-adaptive while still making a near-linear number of queries? In this paper, we give the first non-adaptive algorithms for clustering with subset queries. We provide, (i) a non-adaptive algorithm making $O(n \log^2 n \log k)$ queries which improves to $O(n \log k)$ when the cluster sizes are within any constant factor of each other, (ii) for constant $k$, a non-adaptive algorithm making $O(n \log{\log{n}})$ queries. In addition to non-adaptivity, we take into account other practical considerations, such as enforcing a bound on query size. For constant $k$, we give an algorithm making $\smash{\widetilde{O}(n^2/s^2)}$ queries on subsets of size at most $s \leq \sqrt{n}$, which is optimal among all non-adaptive algorithms within a $\log n$-factor. For arbitrary $k$, the dependence varies as $\tilde{O}(n^2/s)$.

In recent years, there has been a surge of interest in the use of machine-learned predictions to bypass worst-case lower bounds for classical problems in combinatorial optimization. So far, the focus has mostly been on online algorithms, where information-theoretic barriers are overcome using predictions about the unknown future. In this paper, we consider the complementary question of using learned information to overcome computational barriers in the form of approximation hardness of polynomial-time algorithms for NP-hard (offline) problems. We show that noisy predictions about the optimal solution can be used to break classical hardness results for maximization problems such as the max-cut problem and more generally, maximization versions of constraint satisfaction problems (CSPs).

We consider the ℓ0-Low Rank Approximation problem, where the input consists of a matrix A ∈ ℝnR×nc and an integer k, and the goal is to find a matrix B of rank at most k that minimizes ‖A — B‖0, which is the number of entries where A and B differ. For any constant k and ɛ > 0, we present a polynomial time (1 + ɛ)- approximation time for this problem, which significantly improves the previous best poly(k)-approximation.

We study polynomial-time approximation algorithms for edge and vertex Sparsest Cut and Small Set Expansion in terms of k, the number of edges or vertices cut in the optimal solution. Our main results are O(polylog k)-approximation algorithms for various versions in this setting. Our techniques involve an extension of the notion of sample sets (Feige and Mahdian STOC’06), originally developed for small balanced cuts, to sparse cuts in general. We then show how to combine this notion of sample sets with two algorithms, one based on an existing framework of LP rounding and another new algorithm based on the cut-matching game, to get such approximation algorithms. Our cut-matching game algorithm can be viewed as a local version of the cut-matching game by Khandekar, Khot, Orecchia and Vishnoi and certifies an expansion of every vertex set of size s in O(logs) rounds. These techniques may be of independent interest. As corollaries of our results, we also obtain an O(logopt) approximation for min-max graph partitioning, where opt is the min-max value of the optimal cut, and improve the bound on the size of multicut mimicking networks computable in polynomial time.

In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla ‍(FOCS 2002), the input is a complete graph where edges are labeled either + or −, and the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the -edges within parts. In recent years, Chawla, Makarychev, Schramm and Yaroslavtsev ‍(STOC 2015) gave a 2.06-approximation by providing a near-optimal rounding of the standard LP, and Cohen-Addad, Lee, Li, and Newman ‍(FOCS 2022, 2023) finally bypassed the integrality gap of 2 for this LP giving a 1.73-approximation for the problem. While introducing new ideas for Correlation Clustering, their algorithm is more complicated than typical approximation algorithms in the following two aspects: (1) It is based on two different relaxations with separate rounding algorithms connected by the round-or-cut procedure. (2) Each of the rounding algorithms has to separately handle seemingly inevitable correlated rounding errors, coming from correlated rounding of Sherali-Adams and other strong LP relaxations. In order to create a simple and unified framework for Correlation Clustering similar to those for typical approximate optimization tasks, we propose the cluster LP as a strong linear program that might tightly capture the approximability of Correlation Clustering. It unifies all the previous relaxations for the problem. It is exponential-sized, but we show that it can be (1+є)-approximately solved in polynomial time for any є > 0, providing the framework for designing rounding algorithms without worrying about correlated rounding errors; these errors are handled uniformly in solving the relaxation. We demonstrate the power of the cluster LP by presenting a simple rounding algorithm, and providing two analyses, one analytically proving a 1.49-approximation and the other solving a factor-revealing SDP to show a 1.437-approximation. Both proofs introduce principled methods by which to analyze the performance of the algorithm, resulting in a significantly improved approximation guarantee. Finally, we prove an integrality gap of 4/3 for the cluster LP, showing our 1.437-upper bound cannot be drastically improved. Our gap instance directly inspires an improved NP-hardness of approximation with a ratio 24/23 ≈ 1.042; no explicit hardness ratio was known before.

We consider the classic correlation clustering problem: Given a complete graph where edges are labelled either + or −, the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the −edges within parts. Recently, Cohen-Addad, Lee and Newman [CLN22] gave a 1.995-approximation for the problem using the Sherali-Adams hierarchy, hence beating the integrality gap of 2 of the classic linear program. We significantly improve upon this result by providing a 1.73-approximation for the problem. Our approach brings together a new preprocessing of correlation clustering instances that enables a new LP formulation which combined with the algorithm from [CLN22] yields the improved bound.

A $\mu$-constrained Boolean MAX-CSP $(\psi)$ instance is a Boolean Max-CSP instance on predicate $\psi:\{0,1\}^{r} \rightarrow\{0,1\}$ where the objective is to find a labeling of relative weight exactly $\mu$ that maximizes the fraction of satisfied constraints. In this work, we study the approximability of constrained Boolean Max-CSPs via SDP hierarchies by relating the integrality gap of $\operatorname{Max}-\operatorname{CSP}(\psi)$ to its $\mu$-dependent approximation curve. Formally, assuming the Small-Set Expansion Hypothesis, we show that it is NP-hard to approximate $\mu$-constrained instances of $\operatorname{MAX}-\operatorname{CSP}(\psi)$ up to factor $\operatorname{Gap}_{\ell, \mu}(\psi) / \log (1 / \mu)^{2}$ (ignoring factors depending on r) for any $\ell \geq \ell(\mu, r)$. Here, $\operatorname{Gap}_{\ell, \mu}(\psi)$ is the optimal integrality gap of $\ell$-round Lasserre relaxation for $\mu$-constrained $\operatorname{MAX}-\operatorname{CSP}(\psi)$ instances. Our results are derived by combining the framework of Raghavendra [STOC 2008] along with more recent advances in rounding Lasserre relaxations and reductions from the Small-Set Expansion (SSE) problem. A crucial component of our reduction is a novel way of composing generic bias-dependent dictatorship tests with SSE, which could be of independent interest.

The Uncapacitated Facility Location (UFL) problem is one of the most fundamental clustering problems: Given a set of clients C and a set of facilities F in a metric space (C ∪ F, dist) with facility costs open : F→ ℝ+, the goal is to find a set of facilities S ⊆ F to minimize the sum of the opening cost open(S) and the connection cost d(S) := Σp∈C minc∈S dist(p,c). An algorithm for UFL is called a Lagrangian Multiplier Preserving (LMP) α approximation if it outputs a solution S ⊆ F satisfying open(S) + d(S) ≤ open(S*) + αd(S*) for any S* ⊆ F. The best-known LMP approximation ratio for UFL is at most 2 by the JMS algorithm of Jain, Mahdian, and Saberi [STOC'02, J.ACM'03] based on the Dual-Fitting technique. The lack of progress on improving the upper bound on αLMP in the last two decades raised the natural question whether αLMP = 2. We answer this question negatively by presenting a (slightly) improved LMP approximation algorithm for UFL. This is achieved by combining the Dual-Fitting technique with Local Search, another popular technique to address clustering problems. In more detail, we use the LMP solution S produced by JMS to seed a local search algorithm. We show that local search substantially improves S unless a big fraction of the connection cost of S is associated with facilities of relatively small opening costs. In the latter case however the analysis of Jain, Mahdian, and Saberi can be improved (i.e., S is cheaper than expected). To summarize: Either S is close enough to the optimum, or it must belong to the local neighborhood of a good enough local optimum. From a conceptual viewpoint, our result gives a theoretical evidence that local search can be enhanced so as to avoid bad local optima by choosing the initial feasible solution with LP-based techniques. Our result directly implies a (slightly) improved approximation for the related k-Median problem, another fundamental clustering problem: Given (C ∪ F, dist) as in a UFL instance and an integer k ∈ ℕ, find S ⊆ F with |S| = k that minimizes d(S). The current best approximation algorithms for k-Median are based on the following framework: use an LMP α approximation algorithm for UFL to build an α approximate bipoint solution for k-Median, and then round it with a ρBR approximate bipoint rounding algorithm. This implies an α · ρBR approximation. The current-best value of ρBR is 1.338 by Byrka, Pensyl, Rybicki, Srinivasan, and Trinh [SODA'15, TALG'17], which yields 2.6742-approximation. Combining their algorithm with our refined LMP algorithm for UFL (replacing JMS) gives a 2.67059-approximation. * The full version of the paper can be accessed at https://arxiv.org/abs/2207.05150

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].

Given $x\in(\mathbb{R}_{\geqslant 0})(_{2}^{[n]})$ recording pairwise distances, the Metric Violation Distance problem asks to compute the $\ell_{0}$ distance between x and the metric cone; i.e., modify the minimum number of entries of x to make it a metric. Due to its large number of applications in various data analysis and optimization tasks, this problem has been actively studied recently. We present an $O(\log n)$-approximation algorithm for METRIC VIOLATION Distance, exponentially improving the previous best approximation ratio of $O(OPT^{1/3})$ of Fan, Raichel, and Van Buskirk [SODA, 2018]. Furthermore, a major strength of our algorithm is its simplicity and running time. We also study the related problem of Ultrametric Violation Distance, where the goal is to compute the $\ell_{0}$ distance to the cone of ultrametrics, and achieve a constant factor approximation algorithm. The ULTRAMETRIC VIOLATION DISTANCE problem can be regarded as an extension of the problem of fitting ultrametrics studied by Ailon and Charikar [SIAM J. Computing, 2011] and by Cohen-Addad, Das, Kipouridis, Parotsidis, and Thorup [FOCS, 2021] from $\ell_{1}$ norm to $\ell_{0}$ norm. We show that this problem can be favorably interpreted as an instance of CORRELATION CLUSTERING with an additional hierarchical structure, which we solve using a new $O(1)$-approximation algorithm for correlation clustering that has the structural property that it outputs a refinement of the optimum clusters. An algorithm satisfying such a property can be considered of independent interest. We also provide an $O(\log n\log\log n)$ approximation algorithm for weighted instances. Finally, we investigate the complementary version of these problems where one aims at choosing a maximum number of entries of x forming an (ultra-)metric. In stark contrast with the minimization versions, we prove that these maximization versions are hard to approximate within any constant factor assuming the Unique Games Conjecture.

Given a complete graph G=(V, E) where each edge is labeled + or −, the CORRELATION CLUSTERING problem asks to partition V into clusters to minimize the number of +edges between different clusters plus the number of −edges within the same cluster. CORRELATION CLUSTERING has been used to model a large number of clustering problems in practice, making it one of the most widely studied clustering formulations. The approximability of CORRELATION CLUSTERING has been actively investigated [BBC04], [CGW05], [ACN08], culminating in a 2.06-approximation algorithm [CMSY15], based on rounding the standard LP relaxation. Since the integrality gap for this formulation is 2, it has remained a major open question to determine if the approximation factor of 2 can be reached, or even breached. In this paper, we answer this question affirmatively by showing that there exists a($1.994+\varepsilon$)-approximation algorithm based on $O(1/\varepsilon^{2})$ rounds of the Sherali-Adams hierarchy. In order to round a solution to the Sherali-Adams relaxation, we adapt the correlated rounding originally developed for CSPs [BRSII], [GSII], [RT12]. With this tool, we reach an approximation ratio of $2+\varepsilon$ for CORRELATION CLUSTERING. To breach this ratio, we go beyond the traditional triangle-based analysis by employing a global charging scheme that amortizes the total cost of the rounding across different triangles.

We study several questions related to diversifying search results. We give improved approximation algorithms in each of the following problems, together with some lower bounds. - We give a polynomial-time approximation scheme (PTAS) for a diversified search ranking problem [Bansal et al., ICALP 2010] whose objective is to minimizes the discounted cumulative gain. Our PTAS runs in time $n^{2^{O(\log(1/ε)/ε)}} \cdot m^{O(1)}$ where $n$ denotes the number of elements in the databases. Complementing this, we show that no PTAS can run in time $f(ε) \cdot (nm)^{2^{o(1/ε)}}$ assuming Gap-ETH; therefore our running time is nearly tight. Both of our bounds answer open questions of Bansal et al. - We next consider the Max-Sum Dispersion problem, whose objective is to select $k$ out of $n$ elements that maximizes the dispersion, which is defined as the sum of the pairwise distances under a given metric. We give a quasipolynomial-time approximation scheme for the problem which runs in time $n^{O_ε(\log n)}$. This improves upon previously known polynomial-time algorithms with approximate ratios 0.5 [Hassin et al., Oper. Res. Lett. 1997; Borodin et al., ACM Trans. Algorithms 2017]. Furthermore, we observe that known reductions rule out approximation schemes that run in $n^{\tilde{o}_ε(\log n)}$ time assuming ETH. - We consider a generalization of Max-Sum Dispersion called Max-Sum Diversification. In addition to the sum of pairwise distance, the objective includes another function $f$. For monotone submodular $f$, we give a quasipolynomial-time algorithm with approximation ratio arbitrarily close to $(1 - 1/e)$. This improves upon the best polynomial-time algorithm which has approximation ratio $0.5$ by Borodin et al. Furthermore, the $(1 - 1/e)$ factor is tight as achieving better-than-$(1 - 1/e)$ approximation is NP-hard [Feige, J. ACM 1998].

k-median and k-means are the two most popular objectives for clustering algorithms. Despite intensive effort, a good understanding of the approximability of these objectives, particularly in ℓp-metrics, remains a major open problem. In this paper, we significantly improve upon the hardness of approximation factors known in literature for these objectives in ℓp-metrics. We introduce a new hypothesis called the Johnson Coverage Hypothesis (JCH), which roughly asserts that the well-studied Max k-Coverage problem on set systems is hard to approximate to a factor greater than (1–1/e), even when the membership graph of the set system is a subgraph of the Johnson graph. We then show that together with generalizations of the embedding techniques introduced by Cohen-Addad and Karthik (FOCS '19), JCH implies hardness of approximation results for k-median and k-means in ℓp-metrics for factors which are close to the ones obtained for general metrics. In particular, assuming JCH we show that it is hard to approximate the k-means objective: Discrete case: To a factor of 3.94 in the ℓ1-metric and to a factor of 1.73 in the ℓ2-metric; this improves upon the previous factor of 1.56 and 1.17 respectively, obtained under the Unique Games Conjecture (UGC). Continuous case: To a factor of 2.10 in the ℓ1-metric and to a factor of 1.36 in the ℓ2-metric; this improves upon the previous factor of 1.07 in the ℓ2-metric obtained under UGC (and to the best of our knowledge, the continuous case of k-means in ℓ1-metric was not previously analyzed in literature). We also obtain similar improvements under JCH for the k-median objective. Additionally, we prove a weak version of JCH using the work of Dinur et al. (SICOMP ‘05) on Hypergraph Vertex Cover, and recover all the results stated above of Cohen-Addad and Karthik (FOCS ‘19) to (nearly) the same inapproximability factors but now under the standard NP ≠ P assumption (instead of UGC). Finally, we establish a strong connection between JCH and the long standing open problem of determining the Hypergraph Turán number. We then use this connection to prove improved SDP gaps (over the existing factors in literature) for k-means and k-median objectives.

A µ-biased Max-CSP instance with predicate ψ:{0,1}r → {0,1} is an instance of Constraint Satisfaction Problem (CSP) where the objective is to find a labeling of relative weight at most µ which satisfies the maximum fraction of constraints. Biased CSPs are versatile and express several well studied problems such as Densest-k-Sub(Hyper)graph and SmallSetExpansion.

For a family of graphs $$\mathcal {F}$$ , Weighted $$\mathcal {F}$$ -Deletion is the problem for which the input is a vertex weighted graph $$G = (V, E)$$ and the goal is to delete $$S \subseteq V$$ with minimum weight such that $$G \setminus S \in \mathcal {F}$$ . Designing a constant-factor approximation algorithm for large subclasses of perfect graphs has been an interesting research direction. Block graphs, 3-leaf power graphs, and interval graphs are known to admit constant-factor approximation algorithms, but the question is open for chordal graphs and distance-hereditary graphs. In this paper, we add one more class to this list by presenting a constant-factor approximation algorithm when $$\mathcal {F}$$ is the intersection of chordal graphs and distance-hereditary graphs. They are known as ptolemaic graphs and form a superset of both block graphs and 3-leaf power graphs above. Our proof presents new properties and algorithmic results on inter-clique digraphs as well as an approximation algorithm for a variant of Feedback Vertex Set that exploits this relationship (named Feedback Vertex Set with Precedence Constraints), each of which may be of independent interest.

In the k -cut problem, we want to find the lowest-weight set of edges whose deletion breaks a given (multi)graph into k connected components. Algorithms of Karger and Stein can solve this in roughly O ( n 2k ) time. However, lower bounds from conjectures about the k -clique problem imply that Ω ( n (1- o (1)) k ) time is likely needed. Recent results of Gupta, Lee, and Li have given new algorithms for general k -cut in n 1.98k + O(1) time, as well as specialized algorithms with better performance for certain classes of graphs (e.g., for small integer edge weights). In this work, we resolve the problem for general graphs. We show that the Contraction Algorithm of Karger outputs any fixed k -cut of weight α λ k with probability Ω k ( n - α k ), where λ k denotes the minimum k -cut weight. This also gives an extremal bound of O k ( n k ) on the number of minimum k -cuts and an algorithm to compute λ k with roughly n k polylog( n ) runtime. Both are tight up to lower-order factors, with the algorithmic lower bound assuming hardness of max-weight k -clique. The first main ingredient in our result is an extremal bound on the number of cuts of weight less than 2 λ k / k , using the Sunflower lemma. The second ingredient is a fine-grained analysis of how the graph shrinks—and how the average degree evolves—in the Karger process.

The k-means objective is arguably the most widely-used cost function for modeling clustering tasks in a metric space. In practice and historically, k-means is thought of in a continuous setting, namely where the centers can be located anywhere in the metric space. For example, the popular Lloyd's heuristic locates a center at the mean of each cluster. Despite persistent efforts on understanding the approximability of k-means, and other classic clustering problems such as k-median and k-minsum, our knowledge of the hardness of approximation factors of these problems remains quite poor. In this paper, we significantly improve upon the hardness of approximation factors known in the literature for these objectives. We show that if the input lies in a general metric space, it is NP-hard to approximate: Continuous k-median to a factor of 2 – o(1); this improves upon the previous inapproximability factor of 1.36 shown by Guha and Khuller (J. Algorithms '99). Continuous k-means to a factor of 4–o(1); this improves upon the previous inapproximability factor of 2.10 shown by Guha and Khuller (J. Algorithms '99). k-minsum to a factor of 1.415; this improves upon the APX-hardness shown by Guruswami and Indyk (SODA '03). Our results shed new and perhaps counter-intuitive light on the differences between clustering problems in the continuous setting versus the discrete setting (where the candidate centers are given as part of the input).

Consider a random graph model where there is an underlying simple graph G = (V, E), and each edge is sampled independently with probability p ∊ [0, 1]. What is the smallest value of p such that the resulting graph Gp is connected with constant probability? This is a well-studied question for special classes of graphs, such as complete graphs and hypercubes. For instance, when G is the complete graph, we want the connectivity threshold for the Erdős-Rényi G(n, p) model: here the answer is known to be . However, the problem is not well-understood for more general graph classes. We first investigate this connectivity threshold problem for “somewhat dense” graphs. We show that for any and any δ-regular, δ-edge-connected graph G, the random graph Gp for is connected with probability , generalizing upon the case when G is the complete graph. Our proof also bounds the number of approximate mincuts in such a dense graph, which may be of independent interest. Next, for a general graph G with edge connectivity λ, we define an explicit parameter βG ∊ (0, 2 ln n], based on the number of approximate mincuts, and show that there is a sharp transition in the connectivity of G at p = 1 – exp(βG/λ). Moreover, we show that the width of this transition is an additive O(ln λ/λ) term; this improves upon Margulis' classical result bounding the width of the threshold by .

We investigate the approximability of the following optimization problem. The input is an n× n matrix A=(Aij) with real entries and an origin-symmetric convex body K⊂ ℝn that is given by a membership oracle. The task is to compute (or approximate) the maximum of the quadratic form ∑i=1n∑j=1n Aij xixj=⟨ x,Ax⟩ as x ranges over K. This is a rich and expressive family of optimization problems; for different choices of matrices A and convex bodies K it includes a diverse range of optimization problems like max-cut, Grothendieck/non-commutative Grothendieck inequalities, small set expansion and more. While the literature studied these special cases using case-specific reasoning, here we develop a general methodology for treatment of the approximability and inapproximability aspects of these questions.

Given a graph stream, how can we estimate the number of triangles in it using multiple machines with limited storage? Specifically, how should edges be processed and sampled across the machines for rapid and accurate estimation? The count of triangles (i.e., cliques of size three) has proven useful in numerous applications, including anomaly detection, community detection, and link recommendation. For triangle counting in large and dynamic graphs, recent work has focused largely on streaming algorithms and distributed algorithms but little on their combinations for “the best of both worlds.” In this work, we propose CoCoS , a fast and accurate distributed streaming algorithm for estimating the counts of global triangles (i.e., all triangles) and local triangles incident to each node. Making one pass over the input stream, CoCoS carefully processes and stores the edges across multiple machines so that the redundant use of computational and storage resources is minimized. Compared to baselines, CoCoS is: (a) accurate: giving up to smaller estimation error; (b) fast : up to faster, scaling linearly with the size of the input stream; and (c) theoretically sound : yielding unbiased estimates.

Hierarchical Clustering is an unsupervised data analysis method which has been widely used for decades. Despite its popularity, it had an underdeveloped analytical foundation and to address this, Dasgupta recently introduced an optimization viewpoint of hierarchical clustering with pairwise similarity information that spurred a line of work shedding light on old algorithms (e.g., Average-Linkage), but also designing new algorithms. Here, for the maximization dual of Dasgupta’s objective (introduced by Moseley-Wang), we present polynomial-time 42.46% approximation algorithms that use Max-Uncut Bisection as a subroutine. The previous best worst-case approximation factor in polynomial time was 33.6%, improving only slightly over Average-Linkage which achieves 33.3%. Finally, we complement our positive results by providing APX-hardness (even for 0-1 similarities), under the Small Set Expansion hypothesis.

In the k-cut problem, we are given an edge-weighted graph and want to find the least-weight set of edges whose deletion breaks the graph into k connected components. Algorithms due to Karger-Stein and Thorup showed how to find such a minimum k-cut in time approximately O(n 2k−2). The best lower bounds come from conjectures about the solvability of the k-clique problem and a reduction from k-clique to k-cut, and show that solving k-cut is likely to require time Ω(n k ). Our recent results have given special-purpose algorithms that solve the problem in time n 1.98k + O(1), and ones that have better performance for special classes of graphs (e.g., for small integer weights).

Consider a two-stage matching problem, where edges of an input graph are revealed in two stages (batches) and in each stage we have to immediately and irrevocably extend our matching using the edges from that stage. The natural greedy algorithm is half competitive. Even though there is a huge literature on online matching in adversarial vertex arrival model , no positive results were previously known in adversarial edge arrival model . For two-stage bipartite matching problem, we show that the optimal competitive ratio is exactly 2/3 in both the fractional and the randomized-integral models. Furthermore, our algorithm for fractional bipartite matching is instance optimal , i.e., it achieves the best competitive ratio for any given first stage graph. We also study natural extensions of this problem to general graphs and to s stages and present randomized-integral algorithms with competitive ratio ½ + 2− O(s) . Our algorithms use a novel Instance-Optimal-LP and combine graph decomposition techniques with online primal-dual analysis.

We prove that for every constant c and epsilon = (log n)^{-c}, there is no polynomial time algorithm that when given an instance of 3-LIN with n variables where an (1 - epsilon)-fraction of the clauses are satisfiable, finds an assignment that satisfies atleast (1/2 + epsilon)-fraction of clauses unless NP subseteq BPP. The previous best hardness using a polynomial time reduction achieves epsilon = (log log n)^{-c}, which is obtained by the Label Cover hardness of Moshkovitz and Raz [J. ACM, 57(5), 2010] followed by the reduction from Label Cover to 3-LIN of Håstad [J. ACM, 48(4):798 - 859, 2001]. Our main idea is to prove a hardness result for Label Cover similar to Moshkovitz and Raz where each projection has a linear structure. This linear structure of Label Cover allows us to use Hadamard codes instead of long codes, making the reduction more efficient. For the hardness of Linear Label Cover, we follow the work of Dinur and Harsha [SIAM J. Comput., 42(6):2452 - 2486, 2013] that simplified the construction of Moshkovitz and Raz, and observe that running their reduction from a hardness of the problem LIN (of unbounded arity) instead of the more standard problem of solving quadratic equations ensures the linearity of the resultant Label Cover.

In this paper we initiate a systematic study of exact algorithms for some of the well known clustering problems, namely k-MEDIAN and k-MEANS. In k-MEDIAN, the input consists of a set X of n points belonging to a metric space, and the task is to select a subset C ⊆ X of k points as centers, such that the sum of the distances of every point to its nearest center is minimized. In k-MEANS, the objective is to minimize the sum of squares of the distances instead. It is easy to design an algorithm running in time max_{k ≤ n} {n choose k} n^𝒪(1) = 𝒪^*(2ⁿ) (here, 𝒪^*(⋅) notation hides polynomial factors in n). In this paper we design first non-trivial exact algorithms for these problems. In particular, we obtain an 𝒪^*((1.89)ⁿ) time exact algorithm for k-MEDIAN that works for any value of k. Our algorithm is quite general in that it does not use any properties of the underlying (metric) space - it does not even require the distances to satisfy the triangle inequality. In particular, the same algorithm also works for k-Means. We complement this result by showing that the running time of our algorithm is asymptotically optimal, up to the base of the exponent. That is, unless the Exponential Time Hypothesis fails, there is no algorithm for these problems running in time 2^o(n)⋅n^𝒪(1). Finally, we consider the "facility location" or "supplier" versions of these clustering problems, where, in addition to the set X we are additionally given a set of m candidate centers (or facilities) F, and objective is to find a subset of k centers from F. The goal is still to minimize the k-Median/k-Means/k-Center objective. For these versions we give a 𝒪(2ⁿ (mn)^𝒪(1)) time algorithms using subset convolution. We complement this result by showing that, under the Set Cover Conjecture, the "supplier" versions of these problems do not admit an exact algorithm running in time 2^{(1-ε) n} (mn)^𝒪(1).

A number of recent works have studied algorithms for entrywise ℓp-low rank approximation, namely algorithms which given an n × d matrix A (with n ≥ d), output a rank-k matrix B minimizing ‖A – B‖pp = ∑i, j|Ai, j – Bi, j|p when p > 0; and ‖A – B‖0 = ∑i, j[Ai, j ≠ Bi, j] for p = 0, where [·] is the Iverson bracket, that is, ‖A – B‖0 denotes the number of entries (i, j) for which Ai, j ≠ Bi, j. For p = 1, this is often considered more robust than the SVD, while for p = 0 this corresponds to minimizing the number of disagreements, or robust PCA. This problem is known to be NP-hard for p ∊ {0, 1}, already for k = 1, and while there are polynomial time approximation algorithms, their approximation factor is at best poly(k). It was left open if there was a polynomial-time approximation scheme (PTAS) for ℓp-approximation for any p ≥ 0. We show the following: 1. On the algorithmic side, for p ∊ (0, 2), we give the first npoly(k/ε) time (1 + ε)-approximation algorithm. For p = 0, there are various problem formulations, a common one being the binary setting in which A ∊ {0, 1}n×d and B = U · V, where U ∊ {0, 1}n×k and V ∊ {0, 1}k×d. There are also various notions of multiplication U · V, such as a matrix product over the reals, over a finite field, or over a Boolean semiring. We give the first almost-linear time approximation scheme for what we call the Generalized Binary ℓ0-Rank-k problem, for which these variants are special cases. Our algorithm computes (1 + ε)-approximation in time (1/ε)2O(k)/ε2 · nd1+o(1), where o(1) hides a factor (log log d)1.1 / log d. In addition, for the case of finite fields of constant size, we obtain an alternate PTAS running in time n · dpoly(k/ε). 2. On the hardness front, for p ∊ (1, 2), we show under the Small Set Expansion Hypothesis and Exponential Time Hypothesis (ETH), there is no constant factor approximation algorithm running in time 2kδ for a constant δ > 0, showing an exponential dependence on k is necessary. For p = 0, we observe that there is no approximation algorithm for the Generalized Binary ℓ0-Rank-k problem running in time 22δk for a constant δ > 0. We also show for finite fields of constant size, under the ETH, that any fixed constant factor approximation algorithm requires 2kδ time for a constant δ > 0.

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 study the problem of deleting the smallest set S of vertices (resp. edges) from a given graph G such that the induced subgraph (resp. subgraph) G\S belongs to some class ℋ. We consider the case where graphs in ℋ have treewidth bounded by t, and give a general framework to obtain approximation algorithms for both vertex and edge-deletion settings from approximation algorithms for certain natural graph partitioning problems called k-Subset Vertex Separator and k-Subset Edge Separator, respectively. For the vertex deletion setting, our framework combined with the current best result for k-Subset Vertex Separator, improves approximation ratios for basic problems such as k-Treewidth Vertex Deletion and Planar-ℱ Vertex Deletion. Our algorithms are simpler than previous works and give the first deterministic and uniform approximation algorithms under the natural parameterization. For the edge deletion setting, we give improved approximation algorithms for k-Subset Edge Separator combining ideas from LP relaxations and important separators. We present their applications in bounded-degree graphs, and also give an APX-hardness result for the edge deletion problems.

Given an edge-weighted graph, how many minimum k-cuts can it have? This is a fundamental question in the intersection of algorithms, extremal combinatorics, and graph theory. It is particularly interesting in that the best known bounds are algorithmic: they stem from algorithms that compute the minimum k-cut.

Online contention resolution schemes (OCRSs) were proposed by Feldman, Svensson, and Zenklusen as a generic technique to round a fractional solution in the matroid polytope in an online fashion. It has found applications in several stochastic combinatorial problems where there is a commitment constraint: on seeing the value of a stochastic element, the algorithm has to immediately and irrevocably decide whether to select it while always maintaining an independent set in the matroid. Although OCRSs immediately lead to prophet inequalities, these prophet inequalities are not optimal. Can we instead use prophet inequalities to design optimal OCRSs? We design the first optimal $1/2$-OCRS for matroids by reducing the problem to designing a matroid prophet inequality where we compare to the stronger benchmark of an ex-ante relaxation. We also introduce and design optimal $(1-1/e)$-random order CRSs for matroids, which are similar to OCRSs but the arrival is chosen uniformly at random.

In the k-cut problem, we are given an edge-weighted graph G and an integer k, and have to remove a set of edges with minimum total weight so that G has at least k connected components. The current best algorithms are an O(n(2-o(1))k) randomized algorithm due to Karger and Stein, and an Õ(n2k) deterministic algorithm due to Thorup. Moreover, several 2-approximation algorithms are known for the problem (due to Saran and Vazirani, Naor and Rabani, and Ravi and Sinha). It has remained an open problem to (a) improve the runtime of exact algorithms, and (b) to get better approximation algorithms. In this paper we show an O(kO(k)n(2Ω/3 + o(1))k)-time algorithm for k-cut. Moreover, we show an (1+ε)-approximation algorithm that runs in time O((k/ε)O(k)nk + O(1)), and a 1.81-approximation in fixed-parameter time O(2O(k(2))poly(n)).

In the k-CuT problem, we are given an edge-weighted graph G and an integer k, and have to remove a set of edges with minimum total weight so that G has at least k connected components. Prior work on this problem gives, for all h ∊ [2, k], a (2 – h/k)-approximation algorithm for k-cut that runs in time nO(h). Hence to get a (2 – ε)-approximation algorithm for some absolute constant ε, the best runtime using prior techniques is nO(kε). Moreover, it was recently shown that getting a (2 – ε)-approximation for general k is NP-hard, assuming the Small Set Expansion Hypothesis. If we use the size of the cut as the parameter, an FPT algorithm to find the exact k-Cut is known, but solving the k-CuT problem exactly is W[1]-hard if we parameterize only by the natural parameter of k. An immediate question is: can we approximate k-Cut better in FPT-time, using k as the parameter? We answer this question positively. We show that for some absolute constant ε > 0, there exists a (– ε)-approximation algorithm that runs in time 2O(κ6) · Õ(n4). This is the first FPT algorithm that is parameterized only by k and strictly improves the 2-approximation.

Random (dν, dc)-regular low-density paritycheck (LDPC) codes, where each variable is involved in du parity checks and each parity check involves dcvariables are well-known to achieve the Shannon capacity of the binary symmetric channel, for sufficiently large dνand dc, under exponential time decoding. However, polynomial time algorithms are only known to correct a much smaller fraction of errors. One of the most powerful polynomial-time algorithms with a formal analysis is the linear programming (LP) decoding algorithm of Feldman et al., which is known to correct an Ω(1/dc) fraction of errors. In this paper, we show that fairly powerful extensions of LP decoding, based on the Sherali-Adams and Lasserre hierarchies, fail to correct much more errors than the basic LP-decoder. In particular, we show that: 1) for any values of dνand dc, a linear number of rounds of the Sherali-Adams LP hierarchy cannot correct more than an O(1/dc) fraction of errors on a random (dν, dc)-regular LDPC code; and 2) for any value of du and infinitely many values of dc, a linear number of rounds of the Lasserre SDP hierarchy cannot correct more than an O(1/dc) fraction of errors on a random (dν, dc)-regular LDPC code. Our proofs use a new stretching and collapsing technique that allows us to leverage recent progress in the study of the limitations of LP/SDP hierarchies for Maximum Constraint Satisfaction Problems (Max-CSPs). The problem then reduces to the construction of special balanced pairwise independent distributions for Sherali-Adams and special cosets of balanced pairwise independent subgroups for Lasserre. Our (algebraic) construction for the Lasserre hierarchy is based on designing sets of points in Fqd(for q any power of 2 and d = 2, 3) with special hyperplane-incidence properties-constructions that may be of independent interest. An intriguing consequence of our work is that expansion seems to be both the strength and the weakness of random regular LDPC codes. Some of our techniques are more generally applicable to a large class of Boolean CSPs called Min-Ones. In particular, for k-Hypergraph Vertex Cover, we obtain an improved integrality gap of k -1- ε that holds after a linear number of rounds of the Lasserre hierarchy, for any k = q + 1 with q an arbitrary prime power. The best previous gap for a linear number of rounds was equal to 2 - ε and due to Schoenebeck.

The computational complexity of multicut-like problems may vary significantly depending on whether the terminals are fixed or not. In this work we present a comprehensive study of this phenomenon in two types of cut problems in directed graphs: double cut and bicut. 1. Fixed-terminal edge-weighted double cut is known to be solvable efficiently. We show that fixed-terminal node-weighted double cut cannot be approximated to a factor smaller than 2 under the Unique Games Conjecture (UGC), and we also give a 2-approximation algorithm. For the global version of the problem, we prove an inapproximability bound of 3/2 under UGC. 2. Fixed-terminal edge-weighted bicut is known to have an approximability factor of 2 that is tight under UGC. We show that the global edge-weighted bicut is approximable to a factor strictly better than 2, and that the global node-weighted bicut cannot be approximated to a factor smaller than 3/2 under UGC. 3. In relation to these investigations, we also prove two results on undirected graphs which are of independent interest. First, we show NP-completeness and a tight inapproximability bound of 4/3 for the node-weighted 3-cut problem under UGC. Second, we show that for constant k, there exists an efficient algorithm to solve the minimum {s,t}-separating k-cut problem. Our techniques for the algorithms are combinatorial, based on LPs and based on the enumeration of approximate min-cuts. Our hardness results are based on combinatorial reductions and integrality gap instances.

For an n-variate order-d tensor A, define A_{max} := sup_{||x||_2 = 1} , to be the maximum value taken by the tensor on the unit sphere. It is known that for a random tensor with i.i.d. +1/-1 entries, A_{max} <= sqrt(n.d.log(d)) w.h.p. We study the problem of efficiently certifying upper bounds on A_{max} via the natural relaxation from the Sum of Squares (SoS) hierarchy. Our results include: * When A is a random order-q tensor, we prove that q levels of SoS certifies an upper bound B on A_{max} that satisfies B <= A_{max} * (n/q^(1-o(1)))^(q/4-1/2) w.h.p. Our upper bound improves a result of Montanari and Richard (NIPS 2014) when q is large. * We show the above bound is the best possible up to lower order terms, namely the optimum of the level-q SoS relaxation is at least A_{max} * (n/q^(1+o(1)))^(q/4-1/2). * When A is a random order-d tensor, we prove that q levels of SoS certifies an upper bound B on A_{max} that satisfies B <= A_{max} * (n*polylog/q)^(d/4 - 1/2) w.h.p. For growing q, this improves upon the bound certified by constant levels of SoS. This answers in part, a question posed by Hopkins, Shi, and Steurer (COLT 2015), who tightly characterized constant levels of SoS.

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 study variants of the classic s-t cut problem and prove the following improved hardness results assuming the Unique Games Conjecture (UGC). * For Length-Bounded Cut and Shortest Path Interdiction, we show that both problems are hard to approximate within any constant factor, even if we allow bicriteria approximation. If we want to cut vertices or the graph is directed, our hardness ratio for Length-Bounded Cut matches the best approximation ratio up to a constant. Previously, the best hardness ratio was 1.1377 for Length-Bounded Cut and 2 for Shortest Path Interdiction. * For any constant k >= 2 and epsilon > 0, we show that Directed Multicut with k source-sink pairs is hard to approximate within a factor k - epsilon. This matches the trivial k-approximation algorithm. By a simple reduction, our result for k = 2 implies that Directed Multiway Cut with two terminals (also known as s-t Bicut} is hard to approximate within a factor 2 - epsilon, matching the trivial 2-approximation algorithm. * Assuming a variant of the UGC (implied by another variant of Bansal and Khot), we prove that it is hard to approximate Resource Minimization Fire Containment within any constant factor. Previously, the best hardness ratio was 2. For directed layered graphs with b layers, our hardness ratio Omega(log b) matches the best approximation algorithm. Our results are based on a general method of converting an integrality gap instance to a length-control dictatorship test for variants of the s-t cut problem, which may be useful for other problems.

We consider goods that can be shared with k-hop neighbors (i.e., the set of nodes within k hops from an owner) on a social network. We examine incentives to buy such a good by devising game-theoretic models where each node decides whether to buy the good or free ride. First, we find that social inefficiency, specifically excessive purchase of the good, occurs in Nash equilibria. Second, the social inefficiency decreases as k increases and thus a good can be shared with more nodes. Third, and most importantly, the social inefficiency can also be significantly reduced by charging free riders an access cost and paying it to owners, leading to the conclusion that organizations and system designers should impose such a cost. These findings are supported by our theoretical analysis in terms of the price of anarchy and the price of stability; and by simulations based on synthetic and real social networks.

Given an undirected graph $G = (V_G, E_G)$ and a fixed “pattern” graph $H = (V_H, E_H)$ with $k$ vertices, we consider the $H$-Transversal and $H$-Packing problems. The former asks to find the smallest $S \subseteq V_G$ such that the subgraph induced by $V_G \setminus S$ does not have $H$ as a subgraph, and the latter asks to find the maximum number of pairwise disjoint $k$-subsets $S_1, \ldots, S_m \subseteq V_G$ such that the subgraph induced by each $S_i$ has $H$ as a subgraph. We prove that if $H$ is 2-connected, $H$-Transversal and $H$-Packing are almost as hard to approximate as general $k$-Hypergraph Vertex Cover and $k$-Set Packing, so it is NP-hard to approximate them within a factor of $\Omega (k)$ and $\widetilde \Omega (k)$, respectively. We also show that there is a 1-connected $H$ where $H$-Transversal admits an $O(\log k)$-approximation algorithm, so that the connectivity requirement cannot be relaxed from 2 to 1. For a special case of $H$-Transversal where $H$ is a (family of) cycles, we mention the implication of our result to the related Feedback Vertex Set problem and give a different hardness proof for directed graphs.

The Unique Coverage problem, given a universe $V$ of elements and a collection $E$ of subsets of $V$, asks to find $S \subseteq V$ to maximize the number of $e \in E$ that intersects $S$ in exactly one element. When each $e \in E$ has cardinality at most $k$, it is also known as 1-in-$k$ Hitting Set and admits a simple $\Omega(\frac{1}{\log k})$-approximation algorithm. For constant $k$, we prove that 1-in-$k$ Hitting Set is NP-hard to approximate within a factor $O(\frac{1}{\log k})$. This improves the result of Guruswami and Zhou [Theory Comput., 8 (2012), pp. 239--267], who proved the same result assuming the Unique Games Conjecture. For Unique Coverage, we prove that it is hard to approximate within a factor $O(\frac{1}{\log^{1 - \epsilon} n})$ for any $\epsilon > 0$, unless NP admits quasi-polynomial time algorithms. This improves the results of Demaine et al. [SIAM J. Comput., 38 (2008), pp. 1464--1483], including their $\approx 1/\log^{1/3} n$ inapproximability factor, which was proven under the Random 3SAT Hypothesis. Our simple proof combines ideas from two classical inapproximability results for the Set Cover and Constraint Satisfaction Problems, made efficient by various derandomization methods based on bounded independence.

Given an undirected graph $G = (V_G, E_G)$ and a fixed “pattern” graph $H = (V_H, E_H)$ with $k$ vertices, we consider the $H$-Transversal and $H$-Packing problems. The former asks to find the smallest $S \subseteq V_G$ such that the subgraph induced by $V_G \setminus S$ does not have $H$ as a subgraph, and the latter asks to find the maximum number of pairwise disjoint $k$-subsets $S_1, \ldots, S_m \subseteq V_G$ such that the subgraph induced by each $S_i$ has $H$ as a subgraph. We prove that if $H$ is 2-connected, $H$-Transversal and $H$-Packing are almost as hard to approximate as general $k$-Hypergraph Vertex Cover and $k$-Set Packing, so it is NP-hard to approximate them within a factor of $\Omega (k)$ and $\widetilde \Omega (k)$, respectively. We also show that there is a 1-connected $H$ where $H$-Transversal admits an $O(\log k)$-approximation algorithm, so that the connectivity requirement cannot be relaxed from 2 to 1. For a special case of $H$-Transversal where $H$ is a (family of) cycles, we mention the implication of our result to the related Feedback Vertex Set problem and give a different hardness proof for directed graphs.

The Unique Coverage problem, given a universe V of elements and a collection E of subsets of V, asks to find S ⊆ V to maximize the number of e ∊ E that intersects S in exactly one element. When each e ∊ E has cardinality at most k, it is also known as 1-in-k Hitting Set, and admits a simple -approximation algorithm. For constant k, we prove that 1-in-k Hitting Set is NP-hard to approximate within a factor . This improves the result of Guruswami and Zhou [SODA'11, ToC'12], who proved the same result assuming the Unique Games Conjecture. For Unique Coverage, we prove that it is hard to approximate within a factor for any ∊ > 0, unless NP admits quasipolynomial time algorithms. This improves the results of Demaine et al. [SODA'06, SICOMP'08], including their ≈ 1/log1/3 n inapproximability factor which was proven under the Random 3SAT Hypothesis. Our simple proof combines ideas from two classical inapproximability results for Set Cover and Constraint Satisfaction Problem, made efficient by various derandomization methods based on bounded independence.

Given an undirected graph G=(V,E) and a fixed pattern graph H with k vertices, we consider the H-Transversal and H-Packing problems. The former asks to find the smallest subset S of vertices such that the subgraph induced by V - S does not have H as a subgraph, and the latter asks to find the maximum number of pairwise disjoint k-subsets S1, ..., Sm such that the subgraph induced by each Si has H as a subgraph. We prove that if H is 2-connected, H-Transversal and H-Packing are almost as hard to approximate as general k-Hypergraph Vertex Cover and k-Set Packing, so it is NP-hard to approximate them within a factor of Omega(k) and Omega(k / polylog(k)) respectively. We also show that there is a 1-connected H where H-Transversal admits an O(log k)-approximation algorithm, so that the connectivity requirement cannot be relaxed from 2 to 1. For a special case of H-Transversal where H is a (family of) cycles, we mention the implication of our result to the related Feedback Vertex Set problem, and give a different hardness proof for directed graphs.

A Boolean constraint satisfaction problem (CSP) is called approximation resistant if independently setting variables to 1 with some probability achieves the best possible approximation ratio for the fraction of constraints satisfied. We study approximation resistance of a natural subclass of CSPs that we call Symmetric Constraint Satisfaction Problems (SCSPs), where satisfaction of each constraint only depends on the number of true literals in its scope. Thus a SCSP of arity k can be described by a subset of allowed number of true literals. For SCSPs without negation, we conjecture that a simple sufficient condition to be approximation resistant by Austrin and Hastad is indeed necessary. We show that this condition has a compact analytic representation in the case of symmetric CSPs (depending only on the gap between the largest and smallest numbers in S), and provide the rationale behind our conjecture. We prove two interesting special cases of the conjecture, (i) when S is an interval and (ii) when S is even. For SCSPs with negation, we prove that the analogous sufficient condition by Austrin and Mossel is necessary for the same two cases, though we do not pose an analogous conjecture in general.

Random (dv, dc)-regular LDPC codes (where each variable is involved in dv parity checks and each parity check involves dc variables) are well-known to achieve the Shannon capacity of the binary symmetric channel (for sufficiently large dv and dc) under exponential time decoding. However, polynomial time algorithms are only known to correct a much smaller fraction of errors. One of the most powerful polynomial-time algorithms with a formal analysis is the LP decoding algorithm of Feldman et al. which is known to correct an Ω(1/dc) fraction of errors. In this work, we show that fairly powerful extensions of LP decoding, based on the Sherali-Adams and Lasserre hierarchies, fail to correct much more errors than the basic LP-decoder. In particular, we show that: For any values of dv and dc, a linear number of rounds of the Sherali-Adams LP hierarchy cannot correct more than an O(1/dc) fraction of errors on a random (dv, dc)-regular LDPC code. For any value of dv and infinitely many values of dc a linear number of rounds of the Lasserre SDP hierarchy cannot correct more than an O(1/dc) fraction of errors on a random (dv, dc)-regular LDPC code. Our proofs use a new streching and collapsing technique that allows us to leverage recent progress in the study of the limitations of LP/SDP hierarchies for Maximum Constraint Satisfaction Problems (Max-CSPs). The problem then reduces to the construction of special balanced pairwise independent distributions for Sherali-Adams and special cosets of balanced pairwise independent subgroups for Lasserre. Our (algebraic) construction for the Lasserre hierarchy is based on designing sets of points in (for q any power of 2 and d = 2, 3) with special hyperplane-incidence properties – constructions that may be of independent interest. An intriguing consequence of our work is that expansion seems to be both the strength and the weakness of random regular LDPC codes. Some of our techniques are more generally applicable to a large class of Boolean CSPs called Min-Ones. In particular, for k-Hypergraph Vertex Cover, we obtain an improved integrality gap of k – 1 – ε that holds after a linear number of rounds of the Lasserre hierarchy, for any k = q + 1 with q an arbitrary prime power. The best previous gap for a linear number of rounds was equal to 2 – ε and due to Schoenebeck.

We prove the following hardness result for a natural promise variant of the classical CNF-satisfiability problem: Given a CNF-formula where each clause has width $w$ and the guarantee that there exists an assignment satisfying at least $g = \lceil \frac{w}{2}\rceil -1$ literals in each clause, it is NP-hard to find a satisfying assignment to the formula (that sets at least one literal to true in each clause). On the other hand, when $g = \lceil \frac{w}{2}\rceil$, it is easy to find a satisfying assignment via simple generalizations of the algorithms for 2-Sat. Viewing 2-Sat $\in \mathrm{P}$ as tractability of Sat when 1 in 2 literals are true in every clause, and NP-hardness of 3-Sat as intractability of Sat when 1 in 3 literals are true, our result shows, for any fixed $\varepsilon > 0$, the difficulty of finding a satisfying assignment to instances of “$(2+\varepsilon)$-Sat” where the density of satisfied literals in each clause is guaranteed to exceed $\frac{1}{2+\varepsilon}$. We also strengthen the results to prove that, given a (2k+1)-uniform hypergraph that can be 2-colored such that each edge has perfect balance (at most k+1 vertices of either color), it is NP-hard to find a 2-coloring that avoids a monochromatic edge. In other words, a set system with discrepancy 1 is hard to distinguish from a set system with worst possible discrepancy. Finally, we prove a general result showing the intractability of promise constraint satisfaction problems based on the paucity of certain “weak polymorphisms.” The core of the above hardness results is the claim that the only weak polymorphisms in these particular cases are juntas depending on few variables.

The Graph Pricing problem is among the fundamental problems whose approximability is not well-understood. While there is a simple combinatorial 1/4-approximation algorithm, the best hardness result remains at 1/2 assuming the Unique Games Conjecture (UGC). We show that it is NP-hard to approximate within a factor better than 1/4 under the UGC, so that the simple combinatorial algorithm might be the best possible. We also prove that for any ε > 0, there exists δ > 0 such that the integrality gap of nδ-rounds of the Sherali-Adams hierarchy of linear programming for Graph Pricing is at most 1/4 + ε. This work is based on the effort to view the Graph Pricing problem as a Constraint Satisfaction Problem (CSP) simpler than the standard and complicated formulation. We propose the problem called Generalized Max-Dicut(T), which has a domain size T + 1 for every T ≥ 1. Generalized Max-Dicut(1) is well-known Max-Dicut. There is an approximation preserving reduction from Generalized Max-Dicut on directed acyclic graphs (DAGs) to Graph Pricing, and both our results are achieved through this reduction. Besides its connection to Graph Pricing, the hardness of Generalized Max-Dicut is interesting in its own right since in most arity two CSPs studied in the literature, SDP-based algorithms perform better than LP-based or combinatorial algorithms --- for this arity two CSP, a simple combinatorial algorithm does the best.

We study two natural extensions of Constraint Satisfaction Problems (CSPs). Balance-Max-CSP requires that in any feasible assignment each element in the domain is used an equal number of times. An instance of Hard-Max-CSP consists of soft constraints and hard constraints, and the goal is to maximize the weight of satisfied soft constraints while satisfying all the hard constraints. These two extensions contain many fundamental problems not captured by CSPs, and challenge traditional theories about CSPs in a more general framework.

We study the price of stability in undirected network design games with fair cost sharing. Our work provides multiple new pieces of evidence that the true price of stability, at least for special subclasses of games, may be a constant.

We study a generalization of the famous k-center problem where each object is an affine subspace of dimension Δ, and give either the first or significantly improved algorithms and hardness results for many combinations of parameters. This generalization from points (Δ = 0) is motivated by the analysis of incomplete data, a pervasive challenge in statistics: incomplete data objects in ℝd can be modeled as affine subspaces. We give three algorithmic results for different values of k, under the assumption that all subspaces are axis-parallel, the main case of interest because of the correspondence to missing entries in data tables. 1) k = 1: Two polynomial time approximation schemes which runs in poly (Δ, 1/∊)nd. 2) k = 2: O(Δ1/4)-approximation algorithm which runs in poly(n, d, Δ) 3) General k: Polynomial time approximation scheme which runs in We also prove nearly matching hardness results; in both the general (not necessarily axis-parallel) case (for k ≥ 2) and in the axis-parallel case (for k ≥ 3), the running time of an approximation algorithm with any approximation ratio cannot be polynomial in even one of k and Δ, unless P = NP. Furthermore, assuming that the 3-SAT problem cannot be solved sub-exponentially, the dependence on both k and Δ must be exponential in the general case (in the axis-parallel case, only the dependence on k drops to . The simplicity of the first and the third algorithm suggests that they might be actually used in statistical applications. The second algorithm, which demonstrates a theoretical gap between the axis-parallel and general case for k = 2, displays a strong connection between geometric clustering and classical coloring problems on graphs and hypergraphs, via a new Helly-type theorem.