Abstract

The paper presents a minimization algorithm for Boolean functions whose values are defined only for a small part of their range. In contrast to other known minimization algorithms, it uses the "start big" strategy gradually reducing the dimensionality of a term until an implicant is generated. This approach leads to a very fast solution even for problems with several hundreds of input variables and several hundreds of minterms with defined output values. Its programmed version gave in these cases better results (in terms of runtime and minimality of the solution) than the state-of-the-art ESPRESSO.