Abstract

This paper addresses the problem of robust optimization in large-scale networks of identical processors. General convex optimization problems are considered, where uncertain constraints are distributed to the processors in the network. The processors have to compute a maximizer of a linear objective over the robustly feasible set, defined as the intersection of all locally known feasible sets. We propose a novel asynchronous algorithm, based on outer-approximations of the robustly feasible set, to solve such problems. Each processor stores a small set of linear constraints that form an outer-approximation of the robustly feasible set. Based on its locally available information and the data exchange with neighboring processors, each processor repeatedly updates its local approximation. A computational study for robust linear programming illustrates that the completion time of the algorithm depends primarily on the diameter of the communication graph and is independent of the network size.

Autore Pugliese

• Notarstefano Giuseppe

Tutti gli autori

• M. Burger , G. Notarstefano , F.Allgower

Titolo volume/Rivista

PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL

Anno di pubblicazione

2012

ISSN

0743-1546

ISBN

Non Disponibile

Numero di citazioni Wos

1

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

5

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile