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
Link To Document