Reducing the computational cost in ℓi-norm optimization for cellular automaton model identification

Various methods of identifying cellular automaton models and their state transition functions, in particular, have been proposed. Among these, a method that uses l 1 -norm optimization can be applied to multi-valued cellular automaton models. When solving this optimization problem, it is important to reduce the computational cost as the size of the problem increases. In this paper, we present a method for reducing the number of constraints in the optimization problem using the divide-and-conquer method.