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Two-Level Heaps: A New Priority Queue Structure with Applications to the Single Source Shortest Path Problem

  • K. Subramani
  • Kamesh Madduri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5573)

Abstract

The Single Source Shortest Paths problem with positive edge weights (SSSPP) is one of the more widely studied problems in Operations Research and Theoretical Computer Science [1,2] on account of its wide applicability to practical situations. This problem was first solved in polynomial time by Dijkstra [3], who showed that by extracting vertices with the smallest distance from the source and relaxing its outgoing edges, the shortest path to each vertex is obtained. Variations of this general theme have led to a number of algorithms, which work well in practice [4,5,6]. At the heart of a Dijkstra implementation is the technique used to implement a priority queue. It is well known that using Dijkstra’s approach requires Ω(nlogn) steps on a graph having n vertices, since it essentially sorts vertices based on their distances from the source. Accordingly, the fastest implementation of Dijkstra’s algorithm on a graph with n vertices and m edges should take Ω(m + n·logn) time and consequently the Dijkstra procedure for SSSPP using Fibonacci Heaps is optimal, in the comparison-based model. In this paper, we introduce a new data structure to implement priority queues called Two-Level Heap (TLH) and a new variant of Dijkstra’s algorithm called Phased Dijkstra. We contrast the performance of Dijkstra’s algorithm (both the simple and the phased variants) using a number of data structures to implement the priority queue and empirically establish that Two-Level heaps are far superior to Fibonacci heaps on every graph family considered.

Keywords

Short Path Random Graph Priority Queue Short Path Problem Graph Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • K. Subramani
    • 1
  • Kamesh Madduri
    • 2
  1. 1.LDCSEEWest Virginia UniversityMorgantownUSA
  2. 2.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA

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