Finding Shortest Paths With Computational Geometry

Abstract

We present a heuristic search algorithm for the Rd Manhattan shortest path problem that achieves front-to-front bidirectionality in subquadratic time. In the study of bidirectional search algorithms, front-to-front heuristic computations were thought to be prohibitively expensive (at least quadratic time complexity); our algorithm runs in O(n logd n) time and O(n logd−1 n) space, where n is the number of visited vertices. We achieve this result by embedding the problem in Rd+1 and identifying heuristic calculations as instances of a dynamic closest-point problem, to which we then apply methods from computational geometry.

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