Appears in collection : 2017 - T3 - WS3 - Quantum information theory

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. This is joint work with T. Sutter, P. Esfahani, and J. Lygeros (arXiv:1708. 07311).