We consider a class of popular distributed non-convex optimization problems, in which
agents connected by a network ς collectively optimize a sum of smooth (possibly non …

For optimization of a large sum of functions in a distributed computing environment, we
present a novel communication efficient Newton-type algorithm that enjoys a variety of …

Motivated by recent developments in serverless systems for large-scale computation as well
as improvements in scalable randomized matrix algorithms, we develop OverSketched …

In this work, joint optimization of Power Allocation (PA) and Relay Location (RL) is
investigated for Amplify-and-Forward (AF) cooperative relaying in order to minimize the …

In this chapter we discuss higher-order methods for optimization problems in machine
learning applications. We also present underlying theoretical background as well as …

The world we live in is extremely connected, and it will become even more so in a decade. It
is projected that by 2030, there will be 125 billion interconnected smart devices and objects …

Serverless computing platforms represent the fastest-growing segment of cloud services and
are predicted to dominate the future of cloud computing. However, the real-world …

First-order methods such as Stochastic Gradient Descent are methods of choice for solving
non-convex optimization problems in machine learning. These methods primarily rely on the …

Although current augmented, virtual, and mixed reality (AR/VR/MR) systems are facing
advanced and immersive experience in the entertainment industry with countless media …