Deterministic (1+𝜀)-approximate maximum matching with poly(1/𝜀) passes in the semi-streaming model and beyond


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Date

2022-06

Publication Type

Conference Paper

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yes

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Abstract

We present a deterministic (1+ϵ)-approximate maximum matching algorithm in poly(1/ϵ) passes in the semi-streaming model, solving the long-standing open problem of breaking the exponential barrier in the dependence on 1/ϵ. Our algorithm exponentially improves on the well-known randomized (1/ϵ)O(1/ϵ)-pass algorithm from the seminal work by McGregor [APPROX05], the recent deterministic algorithm by Tirodkar with the same pass complexity [FSTTCS18]. Up to polynomial factors in 1/ϵ, our work matches the state-of-the-art deterministic (logn / loglogn) · (1/ϵ)-pass algorithm by Ahn and Guha [TOPC18], that is allowed a dependence on the number of nodes n. Our result also makes progress on the Open Problem 60 at sublinear.info. Moreover, we design a general framework that simulates our approach for the streaming setting in other models of computation. This framework requires access to an algorithm computing a maximal matching and an algorithm for processing disjoint ( 1 / ϵ)-size connected components. Instantiating our framework in CONGEST yields a (logn, 1/ϵ) round algorithm for computing (1+ϵ)-approximate maximum matching. In terms of the dependence on 1/ϵ, this result improves exponentially state-of-the-art result by Lotker, Patt-Shamir, and Pettie [LPSP15]. Our framework leads to the same quality of improvement in the context of the Massively Parallel Computation model as well.

Publication status

published

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Book title

STOC 2022: Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing

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Volume

Pages / Article No.

248 - 260

Publisher

Association for Computing Machinery

Event

54th Annual ACM SIGACT Symposium on Theory of Computing (STOC 2022)

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Methods

Software

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Subject

Streaming; Matching; Algorithm; Approximation; Deterministic

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Notes

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