Approximating APSP without Scaling: Equivalence of Approximate Min-Plus and Exact Min-Max

Zwick’s (1+ε)-approximation algorithm for the All Pairs Shortest Path (APSP) problem runs in time Õ(nω/ε logW), where ω ≤ 2.373 is the exponent of matrix multiplication and W denotes the largest weight. This can be used to approximate several graph characteristics including the diameter, radius, median, minimum-weight triangle, and minimum-weight cycle in the same time bound. Since Zwick’s algorithm uses the scaling technique, it has a factor logW in the running time. In this paper, we study whether APSP and related problems admit approximation schemes avoiding the scaling technique. That is, the number of arithmetic operations should be independent of W; this is called strongly polynomial. Our main results are as follows. (1) We design approximation schemes in strongly polynomial time O(nω/ε polylog(n/ε)) for APSP on undirected graphs as well as for the graph characteristics diameter, radius, median, minimum-weight triangle, and minimum-weight cycle on directed or undirected graphs. (2) For APSP on directed graphs we design an approximation scheme in strongly polynomial time O(nω + 3/2 ε−1 polylog(n/ε)). This is significantly faster than the best exact algorithm. (3) We explain why our approximation scheme for APSP on directed graphs has a worse exponent than ω: Any improvement over our exponent ω + 3/2 would improve the best known algorithm for Min-Max Product. In fact, we prove that approximating directed APSP and exactly computing the Min-Max Product are equivalent. Our techniques yield a framework for approximation problems over the (min,+)-semiring that can be applied more generally. In particular, we obtain the first strongly polynomial approximation scheme for Min-Plus Convolution in strongly subquadratic time, and we prove an equivalence of approximate Min-Plus Convolution and exact Min-Max Convolution.