Feasibility problems, in this context, focus on locating a point within the intersection of several convex sets and lie at the heart of a variety of applications from machine learning to signal ...

Graph Optimisation: The process of identifying the best solution among many possibilities in graph-structured problems, typically to optimise parameters such as cost, distance or connectivity.

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...

Course Description. This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; ...

Non-convex optimization is now ubiquitous in machine learning. While previously, the focus was on convex relaxation methods, now the emphasis is on being able to solve non-convex problems directly.

This course will provide a rigorous introduction to the rich field of convex analysis, particularly as it relates to mathematical optimization and duality theory. In addition to formal analytical ...