An Algorithm to Find Optimum Support-Reducing Decompositions for Index Generation Functions

Tsutomu Sasao, Kyu Matsuura and Yukihiro Iguchi
Dept. of Computer Science, Meiji University, Kawasaki 214-8571, Japan


Index generation functions are useful for pattern matching. This paper presents an algorithm to find supportreducing decompositions for index generation functions. Let n be the number of the input variables, and let s be the number of bound variables. Then, the exhaustive search for finding an optimum support-reducing decomposition requires to check [n || s] combinations. We found a special property of index generation functions that drastically reduces this search space. With this property, we developed a fast algorithm. For a given number of bound variables, it finds a decomposition with the fewest rails. Experimental results up to n = 60 and s = 33 are shown.

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