This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

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 …

4 Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A
Survey Page 1 4 Incremental Gradient, Subgradient, and Proximal Methods for Convex …

We consider the minimization of a sum i= 1^ mf_i (x) consisting of a large number of convex
component functions fi. For this problem, incremental methods consisting of gradient or …

Distributed processing over networks relies on in-network processing and cooperation
among neighboring agents. Cooperation is beneficial when agents share a common …

Purpose: To describe and mathematically validate the superiorization methodology, which is
a recently developed heuristic approach to optimization, and to discuss its applicability to …

The projected subgradient method for constrained minimization repeatedly interlaces
subgradient steps for the objective function with projections onto the feasible region, which …

We study the privatization of distributed learning and optimization strategies. We focus on
differential privacy schemes and study their effect on performance. We show that the popular …

Incremental methods are widely utilized for solving finite-sum optimization problems in
machine learning and signal processing. In this paper, we study a family of incremental …

Minimization problems often occur in modeling phenomena dealing with real-life
applications that nowadays handle large-scale data and require real-time solutions. For …