![]() Many strategies can be further extended and we do not have a complete theory of all Sudoku puzzles. This strategy list is by no means complete. These are now included for the first time on this site. There are naturally special strategies for Jigsaw and Killers because of their differences. They are definitely worth presenting as a demonstration of people's ingenuity but you will only need to have recourse to them on the extreme puzzles. Do read the introductory articles Introducing Chains and Links and Weak and Strong Links.Įxotic strategies do overlap with chaining ones, but they have a peculiar flavour of their own and some wonderful, if obscure, logic. Thus, for example, Remote Pairs are a subset of XY-Chains that is, XY-Chains is a more general approach of which Remote Pairs are a specific instance. You will find, if you read through this group, that earlier strategies become part of a more general theory as the theme develops. This theme is all about bi-value (only two candidates left in the same cell) and bi-location (only two occurrences of a particular candidate left in the same unit) pairs and the incredible number of deductions one can make from them. IEICE Trans.With chaining strategies, there is definitely a theme going through them. Yato, T., Seta, T.: Complexity and completeness of finding another solution and its application to puzzles. on Logic for Programming, Artificial Intelligence, and Reasoning, Short Papers, pp. Stephens, P.: Mastering Sudoku Week By Week. Modelling and Reformulating Constraint Satisfaction Problems, pp. Simonis, H.: Sudoku as a constraint problem. Rosenhouse, J., Taalman, L.: Section 7.3: Sudoku as a problem in graph coloring. Ostrowski, R., Paris, L.: From XSAT to SAT by exhibiting equivalencies. Moraglio, A., Togelius, J.: Geometric particle swarm optimization for the Sudoku puzzle. Moon, T.K., Gunther, J.H., Kupin, J.J.: Sinkhorn solves Sudoku. Moon, T.K., Gunther, J.H.: Multiple constraint satisfaction by belief propagation: an example using Sudoku. Mantere, T., Koljonen, J.: Solving, rating and generating Sudoku puzzles with GA. Madsen, B.A.: An algorithm for exact satisfiability analysed with the number of clauses as parameter. Lynce, I., Ouaknine, J.: Sudoku as a SAT problem. Lewis, R.: Metaheuristics can solve sudoku puzzles. Lawler, E.L.: A note on the complexity of the chromatic number problem. Koch, T.: Rapid mathematical programming or how to solve Sudoku puzzles in a few seconds. Hunt, M., Pong, C., Tucker, G.: Difficulty-driven Sudoku puzzle generation. ![]() ![]() Herzberg, A.M., Murty, M.R.: Sudoku squares and chromatic polynomials. Held, M., Karp, R.M.: A dynamic programming approach to sequencing problems. Gordon, P., Longo, F.: MENSA Guide to Solving Sudoku. In: Apolloni, B., Howlett, R.J., Jain, L. Geem, Z.W.: Harmony Search Algorithm for Solving Sudoku. Press (2002), Įppstein, D.: Nonrepetitive paths and cycles in graphs with application to Sudoku (2005)Įppstein, D.: Recognizing partial cubes in quadratic time. (ed.) More Games of No Chance, MSRI Publications, vol. 42, pp. Algorithmica 52(2), 226–249 (2008)īrouwer, A.E.: Sudoku puzzles and how to solve them. Schloss Dagstuhl (2010)ījörklund, A., Husfeldt, T.: Exact algorithms for exact satisfiability and number of perfect matchings. Leibniz International Proceedings in Informatics, vol. 5, pp. ACM 9, 61–63 (1962)ījörklund, A.: Exact covers via determinants. IEEE Signal Processing Lett. 17(1), 40–42 (2010)īellman, R.: Dynamic programming treatment of the travelling salesman problem. Arnold, E., Lucas, S., Taalman, L.: Gröbner basis representations of Sudoku. ![]()
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