Asynchronous computation and gradient compression have emerged as two key techniques
for achieving scalability in distributed optimization for large-scale machine learning. This …

Mini-batch optimization has proven to be a powerful paradigm for large-scale learning.
However, the state-of-the-art parallel mini-batch algorithms assume synchronous operation …

Developments in communication and data storage technologies have made large-scale
data collection more accessible than ever. The transformation of this data into insight or …

This thesis proposes and analyzes several first-order methods for convex optimization,
designed for parallel implementation in shared and distributed memory architectures. The …

Time-delay dynamical systems are used to model many real-world engineering systems,
where the future evolution of a system depends not only on current states but also on the …

Huge-scale optimization problems appear in several applications ranging from machine
learning over large data sets to distributed model predictive control. Classical optimization …

Innovations in numerical optimization, statistics and high performance computing have
enabled tremendous advances in machine learning algorithms, fuelling applications from …

We study a flexible algorithm for minimizing a sum of component functions, each of which
depends on a large number of decision variables. The algorithm combines aspects of …