• DocumentCode
    1204650
  • Title

    Sinkhorn Solves Sudoku

  • Author

    Moon, Todd K. ; Gunther, Jacob H. ; Kupin, Joseph J.

  • Author_Institution
    Electr. & Comput. Eng. Dept., Utah State Univ., Logan, UT
  • Volume
    55
  • Issue
    4
  • fYear
    2009
  • fDate
    4/1/2009 12:00:00 AM
  • Firstpage
    1741
  • Lastpage
    1746
  • Abstract
    The Sudoku puzzle is a discrete constraint satisfaction problem, as is the error correction decoding problem. We propose here an algorithm for solution to the Sinkhorn puzzle based on Sinkhorn balancing. Sinkhorn balancing is an algorithm for projecting a matrix onto the space of doubly stochastic matrices. The Sinkhorn balancing solver is capable of solving all but the most difficult puzzles. A proof of convergence is presented, with some information theoretic connections. A random generalization of the Sudoku puzzle is presented, for which the Sinkhorn-based solver is also very effective.
  • Keywords
    decoding; error correction codes; parity check codes; Sinkhorn solves Sudoku; Sudoku puzzle; belief propagation; discrete constraint satisfaction problem; error correction decoding problem; low-density parity-check decoding; stochastic matrices; Belief propagation; Constraint theory; Decoding; Error correction; Error correction codes; Helium; Jacobian matrices; Moon; Parity check codes; Stochastic processes; Belief propagation (BP); Sinkhorn; Sudoku; constraint satisfaction; low-density parity-check (LDPC) decoding;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2009.2013004
  • Filename
    4804943