Until 2003, most of this work has been done in collaboration with Onésimo Hernandez-Lerma from CINVESTAV (IPN, Mexico)

**Existence of Optimal Stationary policies** We try to find the weakest sufficient conditions that ensure the existence of stationary optimal policies for Markov Control Processes on general Borel state and action spaces, with unbounded costs, non-compact action spaces and the expected average-cost criterion. One way is to formulate the problem as a *linear programming *(LP) problem in an (infinite dimensional) space of probability measures.

**Computational Aspects** We then want to find a *numerical procedure* to compute the optimal value of the infinite dimensional LP. An approximation sheme based on constraint-aggregation and inner approximation permits to provide an iterative procdure in which a sequence of finite dimensional LPs are solved. Asymptotic convergence to the optimal value is guaranteed under very weak assumptions. We also prove asymptotic convergence of the optimal solutions to an optimal solution of the original problem in an appropriate weak topology.

**Other Optimality Criteria** We are particularly interested in the *sample-path average cost *criterion, a much stronger criterion than the expected average cost criterion. Under rather weak assumptions, we prove the existence of a stationary sample-path average-cost optimal policy.