Madhur Tulsiani

CSP gaps and reductions in the lasserre hierarchy

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

Reductions between Expansion Problems

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

Convex Relaxations and Integrality Gaps

We discuss the effectiveness of linear and semidefinite relaxations in approximating the optimum for combinatorial optimization problems. Various hierarchies of these relaxations, such as the ones defined by Lovasz and Schrijver, Sherali and Adams, and Lasserre generate increasingly strong linear and semidefinite programming relaxations starting from a basic one. We survey some positive applications of these hierarchies, where their use yields improved approximation algorithms. We also discuss known lower bounds on the integrality gaps of relaxations arising from these hierarchies, demonstrating limits on the applicability of such hierarchies for certain optimization problems.

Unique games on expanding constraint graphs are easy: extended abstract

We present an efficient algorithm to find a good solution to the Unique Games problem when the constraint graph is an expander. We introduce a new analysis of the standard SDP in this case that involves correlations among distant vertices. It also leads to a parallel repetition theorem for unique games when the graph is an expander.

SDP Gaps from Pairwise Independence

We consider the problem of approximating fixed-predicate constraint satisfaction problems (MAX k-CSPq(P)), where the variables take values from (q) =f0; 1;:::; q 1g, and each constraint is on k variables and is defined by a fixed k-ary predicate P. Familiar problems like MAX 3-SAT and MAX-CUT belong to this category. Austrin and Mossel recently identified a general class of predicates P for which MAX k-CSPq(P) is hard to approximate. They study predicates P : (q) k !f0; 1g such that the set of assignments accepted by P contains the support of a balanced pairwise independent distribution over the domain of the inputs. We refer to such predicates as promising. Austrin and Mossel show that for any promising predicate P, the problem MAX k-CSPq(P) is Unique-Games-hard to approximate better than the trivial approximation obtained by a random assignment. We give an unconditional analogue of this result in a restricted model of computation. We consider the hierarchy of semidefinite relaxations of MAX k-CSPq(P) obtained by augmenting the canonical semidefinite relaxation with the Sherali-Adams hierarchy. We show that for any promising predicate P, the integrality gap remains the same as the approximation ratio achieved by a random assignment, even after W(n) levels of this hierarchy.

Unique games on expanding constraint graphs are easy

We present an efficient algorithm to find a good solution to the Unique Games problem when the constraint graph is an expander. We introduce a new analysis of the standard SDP in this case that involves correlations among distant vertices. It also leads to a parallel repetition theorem for unique games when the graph is an expander.

A characterization of strong approximation resistance

For a predicate f: {-1, 1}k ↦ {0, 1} with ρ(f) = |f-1(1)|/2k, we call the predicate strongly approximation resistant if given a near-satisfiable instance of CSP(f), it is computationally hard to find an assignment such that the fraction of constraints satisfied is outside the range [ρ(f) - Ω(1), ρ(f) + Ω(1)]. We present a characterization of strongly approximation resistant predicates under the Unique Games Conjecture. We also present characterizations in the mixed linear and semi-definite programming hierarchy and the Sherali-Adams linear programming hierarchy. In the former case, the characterization coincides with the one based on UGC. Each of the two characterizations is in terms of existence of a probability measure on a natural convex polytope associated with the predicate. The predicate is called approximation resistant if given a near-satisfiable instance of CSP(f), it is computationally hard to find an assignment such that the fraction of constraints satisfied is at least ρ(f) + Ω(1). When the predicate is odd, i.e. f(-z) = 1 - f(z), ∀z ∈ {-1, 1}k, it is easily observed that the notion of approximation resistance coincides with that of strong approximation resistance. Hence for odd predicates our characterization of strong approximation resistance is also a characterization of approximation resistance.

LS+ Lower Bounds from Pairwise Independence

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

SDP gaps for 2-to-1 and other label-cover variants

In this paper we present semidefinite programming (SDP) gap instances for the following variants of the Label-Cover problem, closely related to the Unique Games Conjecture: (i) 2-to-1 Label-Cover; (ii) 2-to-2 Label-Cover; (iii) α-constraint Label-Cover. All of our gap instances have perfect SDP solutions. For alphabet size K, the integral optimal solutions have value: (i) O(1/√logK); (ii) O(1/ logK); (iii) O(1/√logK). Prior to this work, there were no known SDP gap instances for any of these problems with perfect SDP value and integral optimum tending to 0.

Linear Programming Hierarchies Suffice for Directed Steiner Tree

We demonstrate that l rounds of the Sherali-Adams hierarchy and 2l rounds of the Lovasz-Schrijver hierarchy suffice to reduce the integrality gap of a natural LP relaxation for Directed Steiner Tree in l-layered graphs from \(\Omega(\sqrt k)\) to O(l·logk) where k is the number of terminals. This is an improvement over Rothvoss’ result that 2l rounds of the considerably stronger Lasserre SDP hierarchy reduce the integrality gap of a similar formulation to O(l·logk).