Extreme Points of Sets of Randomized Strategies in Constrained Optimization and Control Problems

DOI10.1137/040605345zbMath1097.90040OpenAlexW2030565368MaRDI QIDQ5317538

Juan González-Hernández, Onésimo Hernández-Lerma

Publication date: 16 September 2005

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/040605345

zbMATH Keywords

allocation problemsextreme points of convex setsconstrained Markov control processesrandomized strategies (relaxed controls)Young's relaxation technique

Mathematics Subject Classification ID

Convex programming (90C25) Markov and semi-Markov decision processes (90C40) Convex sets in topological linear spaces; Choquet theory (46A55)

Related Items (14)

Relaxed large economies with infinite-dimensional commodity spaces: the existence of Walrasian equilibria ⋮ Convex analytic approach to constrained discounted Markov decision processes with non-constant discount factors ⋮ A survey of average cost problems in deterministic discrete-time control systems ⋮ Extreme Occupation Measures in Markov Decision Processes with an Absorbing State ⋮ Optimal policies for constrained average-cost Markov decision processes ⋮ A variational problem determined by probability measures ⋮ The bang-bang, purification and convexity principles in infinite dimensions ⋮ Optimal control problem regularization for the Markov process with finite number of states and constraints ⋮ Constrained Markov decision processes in Borel spaces: from discounted to average optimality ⋮ Towards the optimal control of Markov chains with constraints ⋮ Convergence of Markov decision processes with constraints and state-action dependent discount factors ⋮ Convexity and closure in optimal allocations determined by decomposable measures ⋮ First passage Markov decision processes with constraints and varying discount factors ⋮ Nonzero-sum stochastic differential games with additive structure and average payoffs

This page was built for publication: Extreme Points of Sets of Randomized Strategies in Constrained Optimization and Control Problems