Unique Games Conjecture Research Articles

Finding small feedback arc sets on large graphs

The minimum feedback arc set problem (FASP), which seeks to remove a minimum set of arcs from a directed graph to make the remaining graph acyclic, is fundamental in graph algorithms with many applications in social network analysis, circuit testing, deadlock resolution, algorithmic games, and other fields. However, in theory, FASP is NP-Hard and cannot be approximated within any constant under the Unique-Games Conjecture. In practice, the performance of existing algorithms on large graphs is still unsatisfactory. To solve FASP on large graphs, we propose two novel methods in this paper. First, we systematically study the reduction rules for FASP that can quickly reduce the scale of input graphs without losing optimality. We integrate the reduction rules into an algorithm that can be used as a preprocessor for all existing FASP algorithms to simplify the input graphs. Second, we provide a scalable heuristic algorithm based on the divide-and-conquer method for better solution quality. Extensive experiments on a wide range of real-world graphs show that the reduction algorithm effectively reduces the size of input instances. Furthermore, for half of the circuit benchmark graphs, our final algorithm improves the quality of the existing heuristic solution by 24% to 40% within an acceptable running time.

Boolean Function Analysis on High-Dimensional Expanders

We initiate the study of Boolean function analysis on high-dimensional expanders. We give a random-walk based definition of high-dimensional expansion, which coincides with the earlier definition in terms of two-sided link expanders. Using this definition, we describe an analog of the Fourier expansion and the Fourier levels of the Boolean hypercube for simplicial complexes. Our analog is a decomposition into approximate eigenspaces of random walks associated with the simplicial complexes. Our random-walk definition and the decomposition have the additional advantage that they extend to the more general setting of posets, encompassing both high-dimensional expanders and the Grassmann poset, which appears in recent work on the unique games conjecture. We then use this decomposition to extend the Friedgut–Kalai–Naor theorem to high-dimensional expanders. Our results demonstrate that a constant-degree high-dimensional expander can sometimes serve as a sparse model for the Boolean slice or hypercube, and quite possibly additional results from Boolean function analysis can be carried over to this sparse model. Therefore, this model can be viewed as a derandomization of the Boolean slice, containing only |X(k-1)|=O(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$|X(k-1)|=O(n)$$\\end{document} points in contrast to nk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( {\\begin{array}{c}n\\\\ k\\end{array}}\\right) $$\\end{document} points in the (k)-slice (which consists of all n-bit strings with exactly k ones).

Filling crosswords is very hard

We revisit a classical crossword filling puzzle which already appeared in Garey& Jonhson's book. We are given a grid with n vertical and horizontal slots and a dictionary with m words. We are asked to place words from the dictionary in the slots so that shared cells are consistent. We attempt to pinpoint the source of intractability of this problem by carefully taking into account the structure of the grid graph, which contains a vertex for each slot and an edge if two slots intersect. Our main approach is to consider the case where this graph has a tree-like structure. Unfortunately, if we impose the common rule that words cannot be reused, we discover that the problem remains NP-hard under very severe structural restrictions, namely, if the grid graph is a union of stars and the alphabet has size 2, or the grid graph is a matching (so the crossword is a collection of disjoint crosses) and the alphabet has size 3. The problem does become slightly more tractable if word reuse is allowed, as we obtain an mtw algorithm in this case, where tw is the treewidth of the grid graph. However, even in this case, we show that our algorithm cannot be improved to obtain fixed-parameter tractability. More strongly, we show that under the ETH the problem cannot be solved in time mo(k), where k is the number of horizontal slots of the instance (which trivially bounds tw).Motivated by these mostly negative results, we also consider the much more restricted case where the problem is parameterized by the number of slots n. Here, we show that the problem does become FPT (if the alphabet has constant size), but the parameter dependence is exponential in n2. We show that this dependence is also justified: the existence of an algorithm with running time 2o(n2), even for binary alphabet, would contradict the randomized ETH. After that, we consider an optimization version of the problem, where we seek to place as many words on the grid as possible. Here it is easy to obtain a 12-approximation, even on weighted instances, simply by considering only horizontal or only vertical slots. We show that this trivial algorithm is also likely to be optimal, as obtaining a better approximation ratio in polynomial time would contradict the Unique Games Conjecture. The latter two results apply whether word reuse is allowed or not.Finally, we present some special cases where the problem is decidable in polynomial time. In particular, we present three reductions, one to 2-SAT, the second to Maximum Matching and the third to Exact Matching.

A simple [formula omitted]-approximation algorithm for Split Vertex Deletion

A split graph is a graph whose vertex set can be partitioned into a clique and a stable set. Given a graph G and weight function w:V(G)→Q≥0, the Split Vertex Deletion (SVD) problem asks to find a minimum weight set of vertices X such that G−X is a split graph. It is easy to show that a graph is a split graph if and only if it does not contain a 4-cycle, 5-cycle, or a two edge matching as an induced subgraph. Therefore, SVD admits an easy 5-approximation algorithm. On the other hand, for every δ>0, SVD does not admit a (2−δ)-approximation algorithm, unless P=NP or the Unique Games Conjecture fails.For every ϵ>0, Lokshtanov, Misra, Panolan, Philip, and Saurabh (Lokshtanov et al., 2020) recently gave a randomized(2+ϵ)-approximation algorithm for SVD. In this work we give an extremely simple deterministic (2+ϵ)-approximation algorithm for SVD.

Improved approximation for maximum edge colouring problem

The anti-Ramsey number , a r ( G , H ) is the minimum integer k such that in any edge colouring of G with k colours there is a rainbow subgraph isomorphic to H , namely, a copy of H with each of its edges assigned a different colour. The notion was introduced by Erdös and Simonovits in 1973. Since then the parameter has been studied extensively. The case when H is a star graph was considered by several graph theorists from the combinatorial point of view. Recently this case received the attention of researchers from the algorithm community because of its applications in interface modelling of wireless networks. To the algorithm community, the problem is known as maximum edge q -colouring problem: Find a colouring of the edges of G , maximizing the number of colours satisfying the constraint that each vertex spans at most q colours on its incident edges. It is easy to see that the maximum value of the above optimization problem equals a r ( G , K 1 , q + 1 ) − 1 . In this paper, we study the maximum edge 2-colouring problem from the approximation algorithm point of view. The case q = 2 is particularly interesting due to its application in real-life problems. Algorithmically, this problem is known to be NP-hard for q ≥ 2 . For the case of q = 2 , it is also known that no polynomial-time algorithm can approximate to a factor less than 3 / 2 assuming the unique games conjecture. Feng et al. showed a 2-approximation algorithm for this problem. Later Adamaszek and Popa presented a 5 / 3 -approximation algorithm with the additional assumption that the input graph has a perfect matching. Note that the obvious but the only known algorithm issues different colours to the edges of a maximum matching (say M ) and different colours to the connected components of G ∖ M . In this article, we give a new analysis of the aforementioned algorithm to show that for triangle-free graphs with perfect matching the approximation ratio is 8 / 5 . We also show that this algorithm cannot achieve a factor better than 58 / 37 on triangle free graphs that has a perfect matching. The contribution of the paper is a completely new, deeper and closer analysis of how the optimum achieves a higher number of colours than the matching based algorithm, mentioned above.

Approximating the discrete time-cost tradeoff problem with bounded depth

We revisit the deadline version of the discrete time-cost tradeoff problem for the special case of bounded depth. Such instances occur for example in VLSI design. The depth of an instance is the number of jobs in a longest chain and is denoted by d. We prove new upper and lower bounds on the approximability. First we observe that the problem can be regarded as a special case of finding a minimum-weight vertex cover in a d-partite hypergraph. Next, we study the natural LP relaxation, which can be solved in polynomial time for fixed d and—for time-cost tradeoff instances—up to an arbitrarily small error in general. Improving on prior work of Lovász and of Aharoni, Holzman and Krivelevich, we describe a deterministic algorithm with approximation ratio slightly less than frac{d}{2} for minimum-weight vertex cover in d-partite hypergraphs for fixed d and given d-partition. This is tight and yields also a frac{d}{2}-approximation algorithm for general time-cost tradeoff instances, even if d is not fixed. We also study the inapproximability and show that no better approximation ratio than frac{d+2}{4} is possible, assuming the Unique Games Conjecture and text {P}ne text {NP}. This strengthens a result of Svensson [21], who showed that under the same assumptions no constant-factor approximation algorithm exists for general time-cost tradeoff instances (of unbounded depth). Previously, only APX-hardness was known for bounded depth.

Linear‐time algorithms for eliminating claws in graphs

AbstractSince many ‐complete graph problems are polynomial‐time solvable when restricted to claw‐free graphs, we study the problem of determining the distance of a given graph to a claw‐free graph, considering vertex elimination a measure. Claw‐free Vertex Deletion (CFVD) consists of determining the minimum number of vertices to be removed from a graph such that the resulting graph is claw‐free. Although CFVD is ‐hard in general and recognizing claw‐free graphs is still a challenge, where the current best deterministic algorithm for a graph G consists of performing executions of the best algorithm for matrix multiplication, we present linear‐time algorithms for CFVD on weighted block graphs and weighted graphs with bounded treewidth. Furthermore, we show that this problem on forests can be solved in linear time by a simpler algorithm, and we determine the exact values for full k‐ary trees. On the other hand, we show that CFVD is ‐hard even when the input graph is a split graph. We also show that the problem is hard to be approximated within any constant factor better than 2, assuming the unique games conjecture.

Target set selection for conservative populations

Let G=(V,E) be a graph on n vertices, where d(v) denotes the degree of vertex v, and t(v) is a threshold associated with v. We consider a process in which initially a set S of vertices becomes active, and thereafter, in discrete time steps, every vertex v that has at least t(v) active neighbors becomes active as well. The set S is contagious if eventually all V becomes active. The target set selection problem TSS asks for the smallest contagious set. TSS is NP-hard and moreover, notoriously difficult to approximate.In the conservative special case of TSS, t(v)>12d(v) for every v∈V. In this special case, TSS can be approximated within a ratio of O(Δ), where Δ=maxv∈V[d(v)]. In this work we introduce a more general class of TSS instances that we refer to as conservative on average (CoA), that satisfy the condition ∑v∈Vt(v)>12∑v∈Vd(v). We design approximation algorithms for some subclasses of CoA. For example, if t(v)≥12d(v) for every v∈V, we can find in polynomial time a contagious set of size ÕΔ⋅OPT2, where OPT is the size of a smallest contagious set in G. We also provide several hardness of approximation results. For example, assuming the unique games conjecture, we prove that TSS on CoA instances with Δ≤3 cannot be approximated within any constant factor.We also present results concerning the fixed parameter tractability of CoA TSS instances.

Convergence rate of block-coordinate maximization Burer–Monteiro method for solving large SDPs

Semidefinite programming (SDP) with diagonal constraints arise in many optimization problems, such as Max-Cut, community detection and group synchronization. Although SDPs can be solved to arbitrary precision in polynomial time, generic convex solvers do not scale well with the dimension of the problem. In order to address this issue, Burer and Monteiro (Math Program 95(2):329–357, 2003) proposed to reduce the dimension of the problem by appealing to a low-rank factorization and solve the subsequent non-convex problem instead. In this paper, we present coordinate ascent based methods to solve this non-convex problem with provable convergence guarantees. More specifically, we prove that the block-coordinate maximization algorithm applied to the non-convex Burer–Monteiro method globally converges to a first-order stationary point with a sublinear rate without any assumptions on the problem. We further show that this algorithm converges linearly around a local maximum provided that the objective function exhibits quadratic decay. We establish that this condition generically holds when the rank of the factorization is sufficiently large. Furthermore, incorporating Lanczos method to the block-coordinate maximization, we propose an algorithm that is guaranteed to return a solution that provides \(1-{\mathcal {O}}\left( 1/r\right) \) approximation to the original SDP without any assumptions, where r is the rank of the factorization. This approximation ratio is known to be optimal (up to constants) under the unique games conjecture, and we can explicitly quantify the number of iterations to obtain such a solution.

The Small Set Vertex Expansion Problem

In the Small Set Vertex Expansion (SSVE) problem, we are given a graph G=(V,E) and a positive integer k≤|V(G)|2, the goal is to return a set S⊂V(G) of k nodes minimizing the vertex expansion ΦV(S)=|N(S)|. The Small Set Vertex Expansion problem has not been as well studied as its edge-based counterpart Small Set Expansion (SSE). SSE, and SSVE to a less extend, have been studied due to their connection to other hard problems including the Unique Games Conjecture and Graph Colouring. The Small Set Vertex Expansion is known to be NP-complete. We enhance our understanding of the problem from the viewpoint of parameterized complexity by showing that (1) the problem is W[1]-hard when parameterized by the solution size, (2) the problem is fixed-parameter tractable (FPT) when parameterized by the neighbourhood diversity of the input graph, (3) it can be solved in polynomial time for graphs of bounded clique-width, and (4) it is fixed-parameter tractable (FPT) when parameterized by treewidth of the input graph.

The Quest for Strong Inapproximability Results with Perfect Completeness

The Unique Games Conjecture has pinned down the approximability of all constraint satisfaction problems (CSPs), showing that a natural semidefinite programming relaxation offers the optimal worst-case approximation ratio for any CSP. This elegant picture, however, does not apply for CSP instances that are perfectly satisfiable, due to the imperfect completeness inherent in the Unique Games Conjecture. This work is motivated by the pursuit of a better understanding of the approximability of perfectly satisfiable instances of CSPs. We prove that an “almost Unique” version of Label Cover can be approximated within a constant factor on satisfiable instances. Our main conceptual contribution is the formulation of a (hypergraph) version of Label Cover that we call V Label Cover . Assuming a conjecture concerning the inapproximability of V Label Cover on perfectly satisfiable instances, we prove the following implications: • There is an absolute constant c 0 such that for k ≥ 3, given a satisfiable instance of Boolean k -CSP, it is hard to find an assignment satisfying more than c 0 k 2 /2 k fraction of the constraints. • Given a k -uniform hypergraph, k ≥ 2, for all ε > 0, it is hard to tell if it is q -strongly colorable or has no independent set with an ε fraction of vertices, where q =⌈ k +√ k -1/2⌉. • Given a k -uniform hypergraph, k ≥ 3, for all ε > 0, it is hard to tell if it is ( k -1)-rainbow colorable or has no independent set with an ε fraction of vertices.

Tight approximation bounds for maximum multi-coverage

In the classic maximum coverage problem, we are given subsets $T_1, \dots, T_m$ of a universe $[n]$ along with an integer $k$ and the objective is to find a subset $S \subseteq [m]$ of size $k$ that maximizes $C(S) := |\cup_{i \in S} T_i|$. It is well-known that the greedy algorithm for this problem achieves an approximation ratio of $(1-e^{-1})$ and there is a matching inapproximability result. We note that in the maximum coverage problem if an element $e \in [n]$ is covered by several sets, it is still counted only once. By contrast, if we change the problem and count each element $e$ as many times as it is covered, then we obtain a linear objective function, $C^{(\infty)}(S) = \sum_{i \in S} |T_i|$, which can be easily maximized under a cardinality constraint. We study the maximum $\ell$-multi-coverage problem which naturally interpolates between these two extremes. In this problem, an element can be counted up to $\ell$ times but no more; hence, we consider maximizing the function $C^{(\ell)}(S) = \sum_{e \in [n]} \min\{\ell, |\{i \in S : e \in T_i\}| \}$, subject to the constraint $|S| \leq k$. Note that the case of $\ell = 1$ corresponds to the standard maximum coverage setting and $\ell = \infty$ gives us a linear objective. We develop an efficient approximation algorithm that achieves an approximation ratio of $1 - \frac{\ell^{\ell}e^{-\ell}}{\ell!}$ for the $\ell$-multi-coverage problem. In particular, when $\ell = 2$, this factor is $1-2e^{-2} \approx 0.73$ and as $\ell$ grows the approximation ratio behaves as $1 - \frac{1}{\sqrt{2\pi \ell}}$. We also prove that this approximation ratio is tight, i.e., establish a matching hardness-of-approximation result, under the Unique Games Conjecture.

Warm-starting quantum optimization

There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for instance in the form of higher-dimensional matrix-valued problems (semidefinite programming). Under the Unique Games Conjecture, these relaxations often provide the best performance ratios available classically in polynomial time. Here, we discuss how to warm-start quantum optimization with an initial state corresponding to the solution of a relaxation of a combinatorial optimization problem and how to analyze properties of the associated quantum algorithms. In particular, this allows the quantum algorithm to inherit the performance guarantees of the classical algorithm. We illustrate this in the context of portfolio optimization, where our results indicate that warm-starting the Quantum Approximate Optimization Algorithm (QAOA) is particularly beneficial at low depth. Likewise, Recursive QAOA for MAXCUT problems shows a systematic increase in the size of the obtained cut for fully connected graphs with random weights, when Goemans-Williamson randomized rounding is utilized in a warm start. It is straightforward to apply the same ideas to other randomized-rounding schemes and optimization problems.

2-Approximating Feedback Vertex Set in Tournaments

A tournament is a directed graph T such that every pair of vertices is connected by an arc. A feedback vertex set is a set S of vertices in T such that T − S is acyclic. We consider the Feedback Vertex Set problem in tournaments. Here, the input is a tournament T and a weight function w : V ( T ) → N, and the task is to find a feedback vertex set S in T minimizing w ( S ) = ∑ v∈S w ( v ). Rounding optimal solutions to the natural LP-relaxation of this problem yields a simple 3-approximation algorithm. This has been improved to 2.5 by Cai et al. [SICOMP 2000], and subsequently to 7/3 by Mnich et al. [ESA 2016]. In this article, we give the first polynomial time factor 2-approximation algorithm for this problem. Assuming the Unique Games Conjecture, this is the best possible approximation ratio achievable in polynomial time.

Three candidate plurality is stablest for small correlations

Abstract Using the calculus of variations, we prove the following structure theorem for noise-stable partitions: a partition of n-dimensional Euclidean space into m disjoint sets of fixed Gaussian volumes that maximise their noise stability must be $(m-1)$ -dimensional, if $m-1\leq n$ . In particular, the maximum noise stability of a partition of m sets in $\mathbb {R}^{n}$ of fixed Gaussian volumes is constant for all n satisfying $n\geq m-1$ . From this result, we obtain: (i) A proof of the plurality is stablest conjecture for three candidate elections, for all correlation parameters $\rho $ satisfying $0<\rho <\rho _{0}$ , where $\rho _{0}>0$ is a fixed constant (that does not depend on the dimension n), when each candidate has an equal chance of winning. (ii) A variational proof of Borell’s inequality (corresponding to the case $m=2$ ). The structure theorem answers a question of De–Mossel–Neeman and of Ghazi–Kamath–Raghavendra. Item (i) is the first proof of any case of the plurality is stablest conjecture of Khot-Kindler-Mossel-O’Donnell for fixed $\rho $ , with the case $\rho \to L1^{-}$ being solved recently. Item (i) is also the first evidence for the optimality of the Frieze–Jerrum semidefinite program for solving MAX-3-CUT, assuming the unique games conjecture. Without the assumption that each candidate has an equal chance of winning in (i), the plurality is stablest conjecture is known to be false.

Approximation of set multi-cover via hypergraph matching

For a given b∈Nn and a hypergraph H=(V,E) with maximum degree Δ, the b-set multicover problem which we are concerned with in this paper may be stated as follows: find a minimum cardinality subset C⊆E such that no vertex vi∈V is contained in less than bi hyperedges of C. Peleg, Schechtman, and Wool (1997) conjectured that for any fixed Δ and b=mini⁡bi, the problem cannot be approximated with a ratio strictly smaller than δ:=Δ−b+1, unless P=NP. In this paper, we show that the conjecture of Peleg et al. is not true on ℓ-uniform hypergraphs by presenting a polynomial-time approximation algorithm based on a matching/covering duality for hypergraphs due to Ray-Chaudhuri (1960), which we convert into an approximative form. The given algorithm yields a ratio smaller than (1−b(ℓ+1)Δ)Δb. Moreover, we prove that the lower bound conjectured by Peleg et al. holds for regular hypergraphs under the unique games conjecture.

A Characterization of Hard-to-Cover CSPs

We continue the study of covering complexity of constraint satisfaction problems (CSPs) initiated by Guruswami, Hastad and Sudan [SIAM J. Computing, 31(6):1663--1686, 2002] and Dinur and Kol [In Proc. $28$th IEEE Conference on Computational Complexity, 2013]. The covering number of a CSP instance $\Phi$, denoted by $\nu(\Phi)$ is the smallest number of assignments to the variables of $\Phi$, such that each constraint of $\Phi$ is satisfied by at least one of the assignments. We show the following results regarding how well efficient algorithms can approximate the covering number of a given CSP instance. - Assuming a covering unique games conjecture, introduced by Dinur and Kol, we show that for every non-odd predicate $P$ over any constant sized alphabet and every integer $K$, it is NP-hard to distinguish between $P$-CSP instances (i.e., CSP instances where all the constraints are of type $P$) which are coverable by a constant number of assignments and those whose covering number is at least $K$. Previously, Dinur and Kol, using the same covering unique games conjecture, had shown a similar hardness result for every non-odd predicate over the Boolean alphabet that supports a pairwise independent distribution. Our generalization yields a complete characterization of CSPs over constant sized alphabet $\Sigma$ that are hard to cover since CSP's over odd predicates are trivially coverable with $|\Sigma|$ assignments. - For a large class of predicates that are contained in the $2k$-LIN predicate, we show that it is quasi-NP-hard to distinguish between instances which have covering number at most two and covering number at least $\Omega(\log\log n)$. This generalizes the $4$-LIN result of Dinur and Kol that states it is quasi-NP-hard to distinguish between $4$-LIN-CSP instances which have covering number at most two and covering number at least $\Omega(\log \log\log n)$.

A tight $$\sqrt{2}$$-approximation for linear 3-cut

We investigate the approximability of the linear 3-cut problem in directed graphs. The input here is a directed graph $$D=(V,E)$$ with node weights and three specified terminal nodes $$s,r,t\in V$$ , and the goal is to find a minimum weight subset of non-terminal nodes whose removal ensures that s cannot reach r and t, and r cannot reach t. The precise approximability of linear 3-cut has been wide open until now: the best known lower bound under the unique games conjecture (UGC) was 4 / 3, while the best known upper bound was 2 using a trivial algorithm. In this work we completely close this gap: we present a $$\sqrt{2}$$ -approximation algorithm and show that this factor is tight under UGC. Our contributions are twofold: (1) we analyze a natural two-step deterministic rounding scheme through the lens of a single-step randomized rounding scheme with non-trivial distributions, and (2) we construct integrality gap instances that meet the upper bound of $$\sqrt{2}$$ . Our gap instances can be viewed as a weighted graph sequence converging to a “graph limit structure”. We complement our results by showing connections between the linear 3-cut problem and other fundamental cut problems in directed graphs.

A periodic isoperimetric problem related to the unique games conjecture

We prove the endpoint case of a conjecture of Khot and Moshkovitz related to the unique games conjecture, less a small error. Let n ≥ 2. Suppose a subset Ω of n‐dimensional Euclidean space satisfies −Ω = Ωc and Ω + v = Ωc (up to measure zero sets) for every standard basis vector . For any and for any q ≥ 1, let and let . For any x ∈ ∂Ω, let N(x) denote the exterior normal vector at x such that ‖N(x)‖2 = 1. Let . Our main result shows that B has the smallest Gaussian surface area among all such subsets Ω, less a small error: In particular, Standard arguments extend these results to a corresponding weak inequality for noise stability. Removing the factor 6 × 10−9 would prove the endpoint case of the Khot‐Moshkovitz conjecture. Lastly, we prove a Euclidean analogue of the Khot and Moshkovitz conjecture. The full conjecture of Khot and Moshkovitz provides strong evidence for the truth of the unique games conjecture, a central conjecture in theoretical computer science that is closely related to the P versus NP problem. So, our results also provide evidence for the truth of the unique games conjecture. Nevertheless, this paper does not prove any case of the unique games conjecture.

Towards a characterization of constant-factor approximable finite-valued CSPs

We study the approximability of (Finite-)Valued Constraint Satisfaction Problems (VCSPs) with a fixed finite constraint language Γ consisting of finitary functions on a fixed finite domain. Ene et al. have shown that, under a mild technical condition, the basic LP relaxation is optimal for constant-factor approximation for VCSP(Γ) unless the Unique Games Conjecture fails. Using the algebraic approach to the CSP, we give new natural algebraic conditions for the finiteness of the integrality gap for the basic LP relaxation of VCSP(Γ) and show how this leads to efficient constant-factor approximation algorithms for several examples that cover all previously known cases that are NP-hard to solve to optimality but admit constant-factor approximation. Finally, we show that the absence of another algebraic condition leads to NP-hardness of constant-factor approximation. Thus, our results indicate where the boundary of constant-factor approximability for VCSPs lies.