A Markovian Incremental Stochastic Subgradient Algorithm

In this article, a stochastic incremental subgradient algorithm for the minimization of sum convex functions is introduced. The method sequentially uses partial information, and sequence subgradients determined by general Markov chain. This makes it suitable to be used in networks, where path information flow stochastically selected. We prove convergence weighted objective function, weights are given Cesàro limiting probability distribution Unlike previous works literature, (not necessarily uniform), allowing flexibility method.