Convex programming in optimal control and information theory

Abstract

The main theme of this thesis is the development of computational methods for classes of infinite-dimensional optimization problems arising in optimal control and information theory. The first part of the thesis is concerned with the optimal control of discrete-time continuous space Markov decision processes (MDP). The second part is centred around two fundamental problems in information theory that can be expressed as optimization problems: the channel capacity problem as well as the entropy maximization subject to moment constraints.

arXiv commentPhD thesis, ETH Zurich

References in corpus (7)

• Performance Bounds for the Scenario Approach and an Extension to a Class of Non-convex Programs
• The Stochastic Reach-Avoid Problem and Set Characterization for Diffusions
• Efficient Approximation of Quantum Channel Capacities
• On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems
• A variational approach to path estimation and parameter inference of hidden diffusion processes
• Isospectral flows on a class of finite-dimensional Jacobi matrices
• From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming