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