This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

This article studies a class of nonsmooth decentralized multiagent optimization problems
where the agents aim at minimizing a sum of local strongly-convex smooth components plus …

PEPit is a python package aiming at simplifying the access to worst-case analyses of a large
family of first-order optimization methods possibly involving gradient, projection, proximal, or …

We study the consensus decentralized optimization problem where the objective function is
the average of agents private non-convex cost functions; moreover, the agents can only …

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given
“difficult”(constrained) problem via finding solutions of a sequence of “easier”(often …

Optimization problems in engineering and applied mathematics are typically solved in an
iterative fashion, by systematically adjusting the variables of interest until an adequate …

Although the field of distributed optimization is well developed, relevant literature focused on
the application of distributed optimization to multi-robot problems is limited. This survey …

Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in
silico existence. However, this perspective is inappropriate for many modern computational …

We revisit the recent gradient tracking algorithm for distributed consensus optimization from
a control theoretic viewpoint. We show that the algorithm can be constructed by solving a …

This paper develops and analyzes an online distributed proximal-gradient method (DPGM)
for time-varying composite convex optimization problems. Each node of the network features …