An algorithm to find optimum support-reducing decompositions for index generation functions
Tsutomu Sasao, Kyu Matsuura, Yukihiro Iguchi · 2017
Index generation functions are useful for pattern matching. This paper presents an algorithm to find support-reducing 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 (ns) 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.