Conditional Inapproximability and Limited Independence

Abstract: Understanding the theoretical limitations of efficient computation is one of the most fundamental open problems of modern mathematics. This thesis studies the approximability of intractable optimization problems. In particular, we study so-called Max CSP problems. These are problems in which we are given a set of constraints, each constraint acting on some k variables, and are asked to find an assignment to the variables satisfyingas many of the constraints as possible.A predicate P : [q]ᵏ → {0, 1} is said to be approximation resistant if it is intractable to approximate the corresponding CSP problem to within a factor which is better than what is expected from a completely random assignment to the variables. We prove that if the Unique Games Conjecture is true, then a sufficient condition for a predicate P :[q]ᵏ → {0, 1} to be approximation resistant is that there exists a pairwise independent distribution over [q]ᵏ which is supported on the set of satisfying assignments Pˉ¹(1) of P.We also study predicates P : {0, 1}² → {0, 1} on two boolean variables. The corresponding CSP problems include fundamental computational problems such as Max Cut and Max 2-Sat. For any P, we give an algorithm and a Unique Games-based hardness result. Under a certain geometric conjecture, the ratios of these two results are shown to match exactly. In addition, this result explains why additional constraints beyond the standard “triangle inequalities” do not appear to help when solving these problems. Furthermore,we are able to use the generic hardness result to obtain improved hardness for the special cases of Max 2-Sat and Max 2-And. For Max 2-Sat, we obtain a hardness of αLLZ + ε ≈ 0.94016, where αLLZ is the approximation ratio of the algorithm due to Lewin, Livnat and Zwick. For Max 2-And, we obtain a hardness of 0.87435. For both of these problems, our results surprisingly demonstrate that the special case of balanced instances (instances where every variable occurs positively and negatively equally often) is not the hardest. Furthermore, the result for Max 2-And also shows that Max Cut is not the hardest 2-CSP.Motivated by the result for k-CSP problems, and their fundamental importance in computer science in general, we then study t-wise independent distributions with random support. We prove that, with high probability, poly(q) ・ n² random points in [q]ⁿ can support a pairwise independent distribution. Then, again with high probability, we show that (poly(q) ・n)ᵗ log(nᵗ) random points in [q]ⁿ can support a t-wise independent distribution. For constant t and q, we show that Ω(nᵗ) random points are necessary in order to be able to support a t-wise independent balanced distribution with non-negligible probability. Also, we show that every subset of [q]ⁿ with size at least qⁿ(1−poly(q)ˉᵗ) can support a t-wise independent distribution.Finally, we prove a certain noise correlation bound for low-degree functions with small Fourier coefficients. This type of result is generally useful in hardness of approximation, derandomization, and additive combinatorics.