Many important applications in global optimization, algebra, probability and statistics,
applied mathematics, control theory, financial mathematics, inverse problems, etc. can be …

We consider the class of nonlinear optimal control problems (OCPs) with polynomial data,
ie, the differential equation, state and control constraints, and cost are all described by …

This book is about discrete-time, time-homogeneous, Markov chains (Mes) and their ergodic
behavior. To this end, most of the material is in fact about stable Mes, by which we mean …

We consider the generalized problem of moments (GPM) from a computational point of view
and provide a hierarchy of semidefinite programming relaxations whose sequence of …

We consider reinforcement learning (RL) in Markov Decision Processes in which an agent
repeatedly interacts with an environment that is modeled by a controlled Markov process. At …

In classical Markov decision process (MDP) theory, we search for a policy that, say,
minimizes the expected infinite horizon discounted cost. Expectation is, of course, a risk …

We develop general recursive methods to solve for optimal contracts in dynamic principal-
agent environments with hidden states and hidden actions. Starting from a general …

We consider linear programming (LP) problems in infinite dimensional spaces that are in
general computationally intractable. Under suitable assumptions, we develop an …

This paper considers constrained Markov control processes in Borel spaces, with
unbounded costs. The criterion to be minimized is a long-run expected average cost, and …

Consider a multihop wireless network serving multiple flows in which wireless interference
constraints between links are described by a link-interference graph. The timely-throughput …