Sparse Approximate Solutions to Max-Plus Equations

Nikos Tsilivis, Anastasios Tsiamis, Petros Maragos

Abstract: In this work, we study the problem of finding approximate, with minimum support set, solutions to matrix max-plus equations, which we call sparse approximate solutions. We show how one can obtain such solutions efficiently and in polynomial time for any \(\ell _p\) approximation error. Subsequently, we propose a method for pruning morphological neural networks, based on the developed theory.