Towards Multi-Pass Streaming Lower Bounds for Optimal Approximation of Max-Cut
Lijie Chen, Gillat Kol, Dmitry Paramonov, Raghuvansh R. Saxena, Zhao Song, Huacheng Yu · Society for Industrial and Applied Mathematics eBooks · 2023
We consider the Max-Cut problem, asking how much space is needed by a streaming algorithm in order to estimate the value of the maximum cut in a graph. This problem has been extensively studied over the last decade, and we now have a near-optimal lower bound for one-pass streaming algorithms, showing that they require linear space to guarantee a better-than-2 approximation [50, 52]. This result relies on a lower bound for the cycle-finding problem, showing that it is hard for a one-pass streaming algorithm to find a cycle in a union of matchings. The end-goal of our research is to prove a similar lower bound for multi-pass streaming algorithms that guarantee a better-than-2 approximation for Max-Cut, a highly challenging open problem. In this paper, we take a significant step in this direction, showing that even o(log n)-pass streaming algorithms need nΩ(1) space to solve the cycle-finding problem. Our proof is quite involved, dividing the cycles in the graph into “short” and “long” cycles, and using tailor-made lower bound techniques to handle each case.