Hardness of Approximation in P via Short Cycle Removal: Cycle Detection, Distance Oracles, and Beyond

PROCEEDINGS OF THE 54TH ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING (STOC '22)(2022)

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摘要
We present a new technique for efficiently removing almost all short cycles in a graph without unintentionally removing its triangles. Consequently, triangle finding problems do not become easy even in almost k-cycle free graphs, for any constant k >= 4. Triangle finding is at the base of many conditional lower bounds in P, mainly for distance computation problems, and the existence of many 4- or 5-cycles in a worst-case instance had been the obstacle towards resolving major open questions. Hardness of approximation: Are there distance oracles with m(1+0(1)) preprocessing time and m(0(1)) query time that achieve a constant approximation? Existing algorithms with such desirable time bounds only achieve super-constant approximation factors, while only 3 - epsilon factors were conditionally ruled out (Patrascu, Roditty, and Thorup; FOCS 2012). We prove that no 0(1) approximations are possible, assuming the 3-SUM or APSP conjectures. In particular, we prove that k-approximations require Q(m(1+1/ck)) time, which is tight up to the constant c. The lower bound holds even for the offline version where we are given the queries in advance, and extends to other problems such as dynamic shortest paths. The 4-Cycle problem: An infamous open question in fine-grained complexity is to establish any surprising consequences from a subquadratic or even linear-time algorithm for detecting a 4-cycle in a graph. This is arguably one of the simplest problems without a near-linear time algorithm nor a conditional lower bound. We prove that Omega(m(1.1194)) time is needed for k-cycle detection for all k >= 4, unless we can detect a triangle in root n-degree graphs in O(n(2-delta)) time; a breakthrough that is not known to follow even from optimal matrix multiplication algorithms.
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关键词
Triangle Finding, 4-Cycle Finding, Hardness of Approximation, Fine-Grained Complexity, Shortest Paths
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