Policy-Based Benchmarking of Weak Heaps and Their Relatives,

  • Asger Bruun
  • Stefan Edelkamp
  • Jyrki Katajainen
  • Jens Rasmussen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6049)


In this paper we describe an experimental study where we evaluated the practical efficiency of three worst-case efficient priority queues: 1) a weak heap that is a binary tree fulfilling half-heap ordering, 2) a weak queue that is a forest of perfect weak heaps, and 3) a run-relaxed weak queue that extends a weak queue by allowing some nodes to violate half-heap ordering. All these structures support Delete and Delete-min in logarithmic worst-case time. A weak heap supports Insert and Decrease in logarithmic worst-case time, whereas a weak queue reduces the worst-case running time of Insert to O(1), and a run-relaxed weak queue that of both Insert and Decrease to O(1). As competitors to these structures, we considered a binary heap, a Fibonacci heap, and a pairing heap. Generic programming techniques were heavily used in the code development. For benchmarking purposes we developed several component frameworks that could be instantiated with different policies.


Priority Queue Left Child Element Comparison Component Framework Binomial Tree 
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 2010

Authors and Affiliations

  • Asger Bruun
    • 1
  • Stefan Edelkamp
    • 2
  • Jyrki Katajainen
    • 1
  • Jens Rasmussen
    • 1
  1. 1.Department of Computer ScienceUniversity of CopenhagenCopenhagen EastDenmark
  2. 2.TZIUniversität BremenBremenGermany

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