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 ...

This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization ... point methods), basic applications (in signal processing, machine learning, statistics ...

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. Stanislaw H. Żak, ...

reported a new quantum process tomography method which is based on convex optimization. They demonstrated that this new method is able to minimize the errors between the process matrix and the ...

The goal is to design a large scale optimization algorithm for machine learning tasks that can run on decentralized compute environments (such as peer-to-peer systems). Requires ability to program and ...