Full Publication List →

Constrained

Refereed Book Chapters

  • F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, “Iteration Complexity of a Fixed-Stepsize SQP Method for Nonconvex Optimization with Convex Constraints,” Numerical Analysis and Optimization, Springer Proceedings in Mathematics & Statistics, pp. 109–120, 2021.
  • G. Scutari and Y. Sun, “Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization,” in Multi-agent Optimization, Lecture Notes in Mathematics, Springer Verlag, CIME Subseries, pp. 141–308, 2018 [DOI].

Journal Papers

  • Y. Sun, G. Scutari, and A. Daneshmand, “Distributed Optimization Based on Gradient-Tracking Revisited: Enhancing Convergence Rate via Surrogation,” SIAM J. on Optimization, vol. 32, no. 2, pp. 354–385, 2022 [DOI].
  • F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, “Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case,” Optimization Methods and Software, vol. 37, no. 4, pp. 1242–1268, 2022 [DOI].
  • F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, “Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity,” Mathematics of Operations Research, vol. 46, no. 2, pp. 595–627, 2021 [DOI].
  • A. Daneshmand, G. Scutari, P. Dvurechensky, A. Gasnikov, “Newton Method over Networks is Fast up to the Statistical Precision,” Proc. International Conference on Machine Learning (ICML 2021), PMLR vol. 139, pp. 2398–2409, 2021.
  • L. Cannelli, F. Facchinei, G. Scutari, and V. Kungurtsev, “Asynchronous Optimization over Graphs: Linear Convergence under Error Bound Conditions,” IEEE Trans. on Automatic Control, vol. 66, no. 10, pp. 4604–4619, Oct. 2021 [DOI].
  • I. Notarnicola, Y. Sun, G. Scutari, and G. Notarstefano, “Distributed Big-Data Optimization via Block-wise Gradient Tracking,” IEEE Trans. on Automatic Control, vol. 66, no. 5, pp. 2045–2060, May 2021 [DOI].
  • L. Cannelli, F. Facchinei, V. Kungurtsev, G. Scutari, “Asynchronous Parallel Algorithms for Nonconvex Optimization,” Mathematical Programming, vol. 184, pp. 121–154, 2020 [DOI].
  • G. Scutari and Y. Sun, “Distributed Nonconvex Constrained Optimization over Time-varying Digraphs,” Mathematical Programming, vol. 176, pp. 497–544, 2019 [DOI].
    Highly cited paper (ISI Web of Knowledge, 2019, 2020).
  • G. Scutari, F. Facchinei, and L. Lampariello, “Parallel and Distributed Methods for Constrained Nonconvex Optimization – Part I: Theory,” IEEE Trans. on Signal Processing, vol. 65, no. 8, pp. 1929–1944, April 2017 [DOI].
    Top 50 downloaded articles in IEEE TSP (Feb. 2017 – June 2018).
    Highly cited paper (ISI Web of Knowledge, 2018, 2019).
  • F. Facchinei, L. Lampariello, and G. Scutari, “Feasible Methods for Nonconvex Nonsmooth Problems with Applications in Green Communications,” Mathematical Programming, vol. 164, pp. 55–90, 2017 [DOI].
  • F. Facchinei, J.-S. Pang, G. Scutari, and L. Lampariello, “VI-constrained Hemivariational Inequalities: Distributed Algorithms and Power Control in Ad-Hoc Networks,” Mathematical Programming, vol. 145, no. 1, pp. 59–96, June 2014 [DOI].
  • I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “Noncooperative Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization with Real-Time Adjustments: A GNEP Approach,” IEEE Trans. on Signal Processing, vol. 62, no. 9, pp. 2397–2412, May 2014 [DOI].
  • A. Alvarado, G. Scutari, and J.-S. Pang, “A New Decomposition Method for Multiuser DC-Programming and Its Applications,” IEEE Trans. on Signal Processing, vol. 62, no. 11, pp. 2984–2998, June 2014 [DOI].
  • Y. Yang, G. Scutari, P. Song, and D. P. Palomar, “Robust MIMO Cognitive Radio under Interference Temperature Constraints,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2465–2482, Nov. 2013 [DOI].
  • G. Scutari and J.-S. Pang, “Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Nash Equilibria and Distributed Algorithms,” IEEE Trans. on Information Theory, vol. 59, no. 7, pp. 4626–4661, July 2013 [DOI].
  • J.-S. Pang and G. Scutari, “Nonconvex Games with Side Constraints,” SIAM J. on Optimization, vol. 21, no. 4, pp. 1491–1522, Dec. 2011 [DOI].
  • J. Wang, G. Scutari, and D. P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” IEEE Transactions on Signal Processing, vol. 59, no. 3, pp. 1182–1201, March 2011 [DOI].
    Top 10 downloaded articles in IEEE TSP (March 2011).
  • J.-S. Pang, G. Scutari, D. P. Palomar, and F. Facchinei, “Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 6, pp. 3251–3271, June 2010 [DOI].
  • G. Scutari and D. P. Palomar, “MIMO Cognitive Radio: A Game-Theoretical Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 2, pp. 761–780, Feb. 2010 [DOI].
    Top 10 downloaded articles in IEEE TSP (Jan.-March 2010).
  • J.-S. Pang, G. Scutari, F. Facchinei, and C. Wang, “Distributed Power Allocation with Rate Constraints in Gaussian Parallel Interference Channels,” IEEE Trans. on Information Theory, vol. 54, no. 8, pp. 3471–3489, Aug. 2008 [DOI].

Refereed Conference Papers

  • J.-S. Pang and G. Scutari, “Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Quasi Nash Equilibria,” IEEE Trans. on Signal Processing, vol. 61, no. 9, pp. 2366–2382, May 2013 [DOI].
  • I. Notarnicola, Y. Sun, G. Scutari, and G. Notarstefano, “Distributed Big-Data Optimization via Block-Iterative Convexification and Averaging,” Proc. of the 56th IEEE Conference on Decision and Control, Dec. 12–15, 2017, Melbourne, Australia.
  • Y. Sun and G. Scutari, “Distributed Nonconvex Optimization for Sparse Representation,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
  • Y. Sun, G. Scutari, and D. Palomar, “Distributed Nonconvex Multiagent Optimization Over Time-Varying Networks,” Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 6–9, 2016, Pacific Grove, CA, USA.
  • G. Scutari, F. Facchinei, L. Lampariello, and P. Song, “Parallel and Distributed Methods for Nonconvex Optimization,” Proc. of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP14), pp. 840–844, May 4–9, 2014, Florence, Italy.
  • J. Wang, G. Scutari, and D. P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” Proc. of the 2010 IEEE International Symposium on Information Theory (ISIT10), pp. 2073–2077, June 13–18, 2010, Austin, Texas, USA.
  • J.-S. Pang, G. Scutari, D. P. Palomar, and F. Facchinei, “Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach,” Proc. of the 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP10), pp. 2997–2998, March 14–19, 2010, Sheraton Dallas Hotel, Dallas, Texas, USA.
  • S. Barbarossa, G. Scutari, and T. Battisti, “Distributed Signal Subspace Projection Algorithms with Maximum Convergence Rate for Sensor Networks with Topological Constraints,” Proc. of the 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP09), pp. 2893–2896, April 19–24, 2009, Taipei, Taiwan.
  • G. Scutari, D. P. Palomar, and S. Barbarossa, “MIMO Cognitive Radio: A Game-Theoretical Approach,” Proc. of the 9th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC08), pp. 426–430, July 7–9, 2008, Recife, Brazil.
  • G. Scutari, S. Barbarossa, and D. P. Palomar, “Potential Games: A Framework for Vector Power Control Problems with Coupled Constraints,” Proc. of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP06), vol. 4, pp. 241–244, May 14–19, 2006, Toulouse, France.
    Best Student Paper Award.