Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization

Recent years have witnessed a surge of interest in parallel and distributed optimization methods for large-scale systems. In particular, nonconvex large-scale optimization problems have found a wide range of applications in several engineering fields. The design and the analysis of such complex, large-scale, systems pose several challenges and call for the development of new optimization … Read more

Distributed Optimization With Local Domain: Applications in MPC and Network Flows

In this paper we consider a network with P nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector x* minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function … Read more

D-ADMM: A Communication-Efficient Distributed Algorithm For Separable Optimization

We propose a distributed algorithm, named D-ADMM, for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem, the cost function is the sum of all the agents’ private cost functions, and the constraint set is the intersection of all the agents’ private constraint sets. We require the private … Read more

A Grid-Enabled Distributed Branch-and-Bound Algorithm with

This work introduces a distributed branch-and-bound algorithm to be run on computational Grids. Grids are often organized in a hierarchical fashion: clusters of processors connected via high-speed links, while the clusters themselves are geographically distant and connected through slower links. Our algorithm does not employ the usual master-worker paradigm and it considers the hierarchical structure … Read more