In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

A systematic understanding of the decomposability structures in network utility maximization
is key to both resource allocation and functionality allocation. It helps us obtain the most …

Many problems in science and engineering can be formulated as optimization problems,
subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually …

In decentralized optimization, nodes cooperate to minimize an overall objective function that
is the sum (or average) of per-node private objective functions. Algorithms interleave local …

In 1963, Polyak proposed a simple condition that is sufficient to show a global linear
convergence rate for gradient descent. This condition is a special case of the Łojasiewicz …

In this monograph, we have attempted to summarize the actively developing field of
statistical learning with sparsity. A sparse statistical model is one having only a small …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

With the advance of unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites,
the integration of space, air and ground networks has become a potential solution to the …

This work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

We combine ideas from machine learning (ML) and operations research and management
science (OR/MS) in developing a framework, along with specific methods, for using data to …