On the Global Convergence of univariate-Dynamic Encoding Algorithm for Searches (uDEAS)

This paper provides a global convergence property of the univariate dynamic encoding algorithm for searches (uDEAS). uDEAS is a recent nonlinear optimization method which treats binary pseudo-matrices as variables to optimize. Since uDEAS can be classified into the generating set search method, the global convergence property of uDEAS is proved in the similar context. The proof of global convergence shows that uDEAS guarantees that an arbitrary starting point can reach a stationary point under general assumptions, which means that uDEAS is a reliable and robust optimization method