Dual decomposition, and more generally Lagrangian relaxation, is a classical method for
combinatorial optimization; it has recently been applied to several inference problems in …

This book, as the title suggests, is about first-order methods, namely, methods that exploit
information on values and gradients/subgradients (but not Hessians) of the functions …

This chapter provides a tutorial overview of distributed optimization and game theory for
decision-making in networked systems. We discuss properties of first-order methods for …

Due to its reduced communication overhead and robustness to failures, distributed energy
management is of paramount importance in smart grids, especially in microgrids, which …

We study a distributed computation model for optimizing a sum of convex objective functions
corresponding to multiple agents. For solving this (not necessarily smooth) optimization …

We consider a general multi-agent convex optimization problem where the agents are to
collectively minimize a global objective function subject to a global inequality constraint, a …

Submodular functions are relevant to machine learning for at least two reasons:(1) some
problems may be expressed directly as the optimization of submodular functions and (2) the …

Various distributed optimization methods have been developed for solving problems which
have simple local constraint sets and whose objective function is the sum of local cost …

The fifth generation wireless networks must provide fast and reliable connectivity while
coping with the ongoing traffic growth. It is of paramount importance that the required …

We study distributed optimization in a cooperative multi-agent setting, where agents have to
agree on the usage of shared resources and can communicate via a time-varying network to …