Distributed Optimization of Large-Scale Complex Networks

Research in this context pursues an ambitious and challenging goal: Targeting provable convergent, low-complexity, distributed solution methods for a very general class of (continuous) nonconvex (nonsmooth) programs, whose nonconvexity occurs at both levels of the objective function and constraints. Instances of such a general formulations arise in many fields of engineering, so diverse as signal processing, communications, networking, machine learning, smart grids, etc. Our main contribution in this context is a new convergent and distributed algorithmic framework for the aforementioned general formulation. In a nutshell, our framework is very general and flexible; it unifies several existing Successive Convex Approximation (SCA)-based algorithms such as (proximal) gradient or Newton type methods, block coordinate (parallel) descent schemes, difference of convex functions methods, and improves on their convergence properties. More importantly, and differently from current SCA schemes, it naturally leads to distributed and parallelizable schemes for a large class of nonconvex problems.

Examples of application of the proposed framework include:
  • Linear transceiver design in cellular systems or ad-hoc networks [1];
  • Cooperative physical layer security optimization [2];
  • Dynamic spectrum and interference management of cognitive radio systems [3];
  • Network Utility Maximization problems for interference SISO/MISO/MIMO ad-hoc (or multi-cell cellular) networks, such as sum-rate maximization, proportional fairness, max-min optimization, and rate profile optimization [1, 4];
  • Stochastic Network Utility Maximization for interference SISO/MISO/MIMO ad-hoc (or multi-cell cellular) networks [5];
  • Joint optimization of computation and communication resources in multiuser mobile cloud computing [6];
  • Joint cell selection and radio resource allocation in MIMO femtocell systems [7];
  • Distributed demand-side management in smart grid [8].

Related Publications

  1. Gesualdo Scutari, Francisco Facchinei, Peiran Song, Daniel P. Palomar, and Jong-Shi Pang, “Decomposition by Partial Linearization: Parallel Optimization of Multiuser Systems,” IEEE Trans. on Signal Processing, vol. 63, no. 3, pp. 641-656, Feb. 2014 [PDF].
  2. Alberth Alvarado, Gesualdo Scutari, and Jong-Shi Pang, "A New Distributed DC-Programming Method and its Application to Physical Layer Security," IEEE Trans. on Signal Processing, vol. 62, no. 11, pp. 2984-2998, June 2014.
  3. Yang Yang, Gesualdo Scutari, Peiran Song, and Daniel P. Palomar, “Robust MIMO Cognitive Radio under Interference Temperature Constraints,” IEEE J. on Selected Areas in Communications, vol. 31, no. 11, pp. 2465–2482, Nov. 2013.
  4. Gesualdo Scutari, Francisco Facchinei, Lorenzo Lampariello, and Peiran Song, “Distributed Methods for Nonconvex Constraints Multi-Agent Problems-Part I: Theory," IEEE Trans. on Signal Processing, (submitted Oct. 2014) [PDF] [Conference Version].
  5. Yang Yang, Gesualdo Scutari, Daniel Palomar, and Marius Pesavento, "A Parallel Stochastic Optimization Method for Nonconvex Multi-Agent Optimization Problems," IEEE Trans. on Signal Processing, (submitted Oct. 2014) [PDF].
  6. Stefania Sardellitti, Gesualdo Scutari, and Sergio Barbarossa, “Joint Optimization of Radio and Computational Resources for Multi-Cell Mobile-Edge Computing,’” IEEE Trans. on Signal and Information Processing over Networks, (submitted Dec. 2014) [PDF].
  7. Stefania Sardellitti, Gesualdo Scutari, and Sergio Barbarossa, “Joint Cell Selection and MIMO Precoding in Heterogeneous Networks via Successive Convex Approximation,” IEEE Trans. on Signal Processing, (submitted Dec. 2013), [Conference Version].
  8. Italo Atzeni, Luis G. Ordonez, Gesualdo Scutari, Daniel P. Palomar, and Javier R. Fonollosa, “Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid,” IEEE Trans. on Signal Processing, vol. 61, no. 10, pp. 2454–2472, May 2013. [Top 10 downloaded articles in IEEE TSP (June, July 2013)].