Streaming approximation resistance of every ordering CSP

An ordering constraint satisfaction problem (OCSP) is given by a positive integer k and a constraint predicate Π mapping permutations on {1, . . . , k} to {0, 1}. Given an instance of OCSP(Π) on n variables and m constraints, the goal is to find an ordering of the n variables that maximizes the number of constraints that are satisfied, where a constraint specifies a sequence of k distinct variables and the constraint is satisfied by an ordering on the n variables if the ordering induced on the k variables in the constraint satisfies Π. Ordering constraint satisfaction problems capture natural problems including “Maximum acyclic subgraph (MAS)” and “Betweenness”. In this work we consider the task of approximating the maximum number of satisfiable constraints in the (single-pass) streaming setting, where an instance is presented as a stream of constraints. We show that for every Π, OCSP(Π) is approximation-resistant to o( √ n)-space streaming algorithms, i.e., algorithms using o( √ n) space cannot distinguish streams where almost every constraint is satisfiable from streams where no ordering beats the random ordering by a noticeable amount. In the case of MAS our result shows that for every ε > 0, MAS is not 1/2 + ε-approximable. The previous best inapproximability result only ruled out a 3/4 approximation. Our results build on a recent work of Chou, Golovnev, Sudan, and Velusamy who show tight inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets. We show that the hard instances from this earlier work have the following “small-set expansion” property: in every partition of the hypergraph formed by the constraints into small blocks, most of the hyperedges are incident on vertices from distinct blocks. By exploiting this combinatorial property, in combination with a natural reduction from CSPs over large finite alphabets to OCSPs, we give optimal inapproximability results for all OCSPs.