Distributed Proximal Algorithms for Large-Scale Structured Optimization KU Leuven
Efficient first-order algorithms for large-scale distributed optimization is the main subject of investigation in this thesis. The algorithms considered cover a wide array of applications in machine learning, signal processing and control. In recent years, a large number of algorithms have been introduced that rely on (possibly a reformulation of) one of the classical splitting algorithms, specifically forward-backward, Douglas-Rachford and ...