Homomorphic Hashing for Sparse Coefficient Extraction

  • Petteri Kaski
  • Mikko Koivisto
  • Jesper Nederlof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7535)


We study classes of Dynamic Programming (DP) algorithms which, due to their algebraic definitions, are closely related to coefficient extraction methods. DP algorithms can easily be modified to exploit sparseness in the DP table through memorization. Coefficient extraction techniques on the other hand are both space-efficient and parallelisable, but no tools have been available to exploit sparseness. We investigate the systematic use of homomorphic hash functions to combine the best of these methods and obtain improved space-efficient algorithms for problems including LINEAR SAT, SET PARTITION and SUBSET SUM. Our algorithms run in time proportional to the number of nonzero entries of the last segment of the DP table, which presents a strict improvement over sparse DP. The last property also gives an improved algorithm for CNF SAT and SET COVER with sparse projections.


Polynomial Space Multiplication Gate Input Gate Ring Element Dynamic Program Table 
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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Petteri Kaski
    • 1
  • Mikko Koivisto
    • 2
  • Jesper Nederlof
    • 3
  1. 1.Helsinki Institute for Information Technology HIIT, Department of Information and Computer ScienceAalto UniversityFinland
  2. 2.Helsinki Institute for Information Technology HIIT, Department of Computer ScienceUniversity of HelsinkiFinland
  3. 3.Utrecht UniversityUtrechtThe Netherlands

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