Approximating Min-Mean-Cycle for low-diameter graphs in near-optimal time and memory
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We revisit Min-Mean-Cycle, the classical problem of finding a cycle in a weighted directed graph with minimum mean weight. Despite an extensive algorithmic literature, previous work falls short of a near-linear runtime in the number of edges m–in fact, there is a natural barrier which precludes such a runtime for solving Min-Mean-Cycle exactly. Here, we give a much faster approximation algorithm that, for graphs with polylogarithmic diameter, has near-linear runtime. In particular, this is the first algorithm whose runtime for the complete graph scales in the number of vertices n as Õ(n^2). Moreover–unconditionally on the diameter–the algorithm uses only O(n) memory beyond reading the input, making it "memory-optimal". The algorithm is also simple to implement and has remarkable practical performance. Our approach is based on solving a linear programming (LP) relaxation using entropic regularization, which effectively reduces the LP to a Matrix Balancing problem–a la the popular reduction of Optimal Transport to Matrix Scaling. We then round the fractional LP solution using a variant of the classical Cycle-Cancelling algorithm that is sped up to near-linear runtime at the expense of being approximate, and implemented in a memory-optimal manner. We also provide an alternative algorithm with slightly faster theoretical runtime, albeit worse memory usage and practicality. This algorithm uses the same rounding procedure, but solves the LP relaxation by leveraging recent developments in area-convexity regularization. Its runtime scales inversely in the approximation accuracy, which we show is optimal–barring a major breakthrough in algorithmic graph theory, namely faster Shortest Paths algorithms.
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