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 ...

Optimization methods form the backbone of numerical analysis, enabling the efficient solution of problems across engineering, data science, physics and beyond. At their core lie gradient-based ...

The goal of this course is to investigate in-depth and to develop expert knowledge in the theory and algorithms for convex optimization. This course will provide a rigorous introduction to the rich ...

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

The course will take an in-depth look at the main concepts and algorithms in convex optimization. The goal is to develop expert knowledge in duality and in the design and analysis of algorithms for ...