TY - GEN

T1 - Two-level heaps

T2 - 3rd International Conference on Combinatorial Optimization and Applications, COCOA 2009

AU - Subramani, K.

AU - Madduri, Kamesh

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=70350654268&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70350654268&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-02026-1_17

DO - 10.1007/978-3-642-02026-1_17

M3 - Conference contribution

AN - SCOPUS:70350654268

SN - 3642020259

SN - 9783642020254

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 186

EP - 196

BT - Combinatorial Optimization and Applications - Third International Conference, COCOA 2009, Proceedings

Y2 - 10 June 2009 through 12 June 2009

ER -