Full Publication List →

• K. Ding and G. Scutari, “From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing,” preprint, arXiv:2610.03709, 2026.
• X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” preprint, arXiv:2605.00711, 2026.
• X. Chen, I. Kuruzov, and G. Scutari, “Adaptive Decentralized Composite Optimization via Three-Operator Splitting,” preprint, arXiv:2602.17545, 2026.

• G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Optimization of Cognitive Radio MIMO Systems via Game Theory,” in Convex Optimization in Signal Processing and Communications, Eds. D. P. Palomar and Yonina C. Eldar, Cambridge University Press, 2009.
• S. Barbarossa, G. Scutari, and L. Pescosolido, “Distributed Space-Time Coding Techniques for Multihop Networks,” in EURASIP Hindawi Book Series, T. Kaiser and A. Bourdoux, Editors, 2004.

• G. Scutari, F. Facchinei, D. P. Palomar, and J.-S. Pang, “Convex Optimization, Game Theory, and Variational Inequality Theory in Multiuser Communication Systems,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 35–49, May 2010 [DOI].
  Top 100 downloaded articles in IEEE among 1.25 million articles available (April-July 2010).
  Top 10 downloaded articles in IEEE SP Magazine (April–August 2010).
• G. Scutari, D. P. Palomar, J.-S. Pang, and F. Facchinei, “Flexible Design for Cognitive Wireless Systems: From Game Theory to Variational Inequality Theory,” IEEE Signal Processing Magazine, vol. 26, no. 5, pp. 107–123, Sept. 2009 [DOI].
  Top 100 downloaded articles in IEEE among 1.25 million articles available (Sept. 2009)
  Top 10 downloaded articles in IEEE SP Magazine (Oct. 2009).
• S. Barbarossa and G. Scutari, “Bio-Inspired Sensor Network Design,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 26–35, May 2007 [DOI].

• T. Cao, X. Chen, and G. Scutari, “DCatalyst: A Unified Accelerated Framework for Decentralized Optimization,” J. of Machine Learning Research, vol. 27, no. 47, pp. 1–57, 2026.
• X. Chen, T. Cao, and G. Scutari, “Enhancing Convergence of Decentralized Gradient Tracking under the KL Property,” IEEE Trans. on Automatic Control, early access, 2026 [DOI].
• I. Kuruzov, G. Scutari, and A. Gasnikov, “Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization,” Proc. Conference on Neural Information Processing Systems (NeurIPS), 2024.
• Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, “Distributed Sparse Regression via Penalization,” J. of Machine Learning Research, vol. 24, no. 272, pp. 1–62, 2023.
• Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, “Distributed (ATC) Gradient Descent for High-Dimension Sparse Regression,” IEEE Trans. on Information Theory, vol. 69, no. 8, pp. 5253–5276, 2023 [DOI].
• V. Kungurtsev, M. Morafah, T. Javidi, and G. Scutari, “Decentralized Asynchronous Non-convex Stochastic Optimization on Directed Graphs,” IEEE Trans. on Control of Network Systems, vol. 10, no. 4, pp. 1796–1804, 2023 [DOI].
• N. Michelusi, G. Scutari, and C.-S. Lee, “Finite-Bit Quantization for Distributed Algorithms with Linear Convergence,” IEEE Trans. on Information Theory, vol. 68, no. 11, pp. 7254–7280, Nov. 2022 [DOI].
• 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].
• Y. Tian, G. Scutari, T. Cao, and A. Gasnikov, “Acceleration in Distributed Optimization under Similarity,” Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 2022.
• 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.
• A. Beznosikov, G. Scutari, A. Rogozin, and A. Gasnikov, “Distributed Saddle-Point Problems under Data Similarity,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
• J. Xu, Y. Tian, Y. Sun, and G. Scutari, “Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis,” IEEE Trans. on Signal Processing, vol. 69, pp. 3555–3570, June 2021 [DOI].
• J. Xu, Y. Tian, Y. Sun, and G. Scutari, “Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks,” Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 2020.
• C.-S. Lee, N. Michelusi, and G. Scutari, “Limited Rate Distributed Weight-Balancing and Average Consensus over Digraphs,” IEEE Trans. on Automatic Control, vol. 66, no. 10, pp. 4530–4545, 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].
• 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).
• A. Daneshmand, Y. Sun, G. Scutari, F. Facchinei, and B. Sadler, “Decentralized Dictionary Learning over Time-Varying Digraphs,” J. of Machine Learning Research, vol. 20, no. 139, pp. 1–62, 2019.
• P. Di Lorenzo and G. Scutari, “NEXT: In-Network Nonconvex Optimization,” IEEE Trans. on Signal and Information Processing over Networks, vol. 2, no. 2, pp. 120–136, June 2016 [DOI].
  Top 50 downloaded articles in IEEE TSIPN (Dec. 2016 – May 2018).
  Highly cited paper (ISI Web of Knowledge, 2017, 2018, 2019).
• Y. Yang, G. Scutari, D. Palomar, and M. Pesavento, “A Parallel Stochastic Approximation Method for Nonconvex Multi-Agent Optimization Problems,” IEEE Trans. on Signal Processing, vol. 64, no. 11, pp. 2949–2964, June 2016 [DOI].
• Z. Guan, T. Melodia, and G. Scutari, “To Transmit or Not to Transmit? Distributed Queueing Games for Infrastructureless Wireless Networks,” IEEE/ACM Trans. on Networking, vol. 24, no. 2, pp. 1153–1166, April 2016 [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].
• 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. 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).
• G. Scutari and S. Barbarossa, “Distributed Consensus Over Wireless Sensor Networks Affected by Multipath Fading,” IEEE Trans. on Signal Processing, vol. 56, no. 8, pp. 4100–4110, Aug. 2008 [DOI].
• G. Scutari, S. Barbarossa, and L. Pescosolido, “Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels,” IEEE Trans. on Signal Processing, vol. 56, no. 4, pp. 1667–1684, April 2008 [DOI].
• S. Barbarossa and G. Scutari, “Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems,” IEEE Trans. on Signal Processing, vol. 55, no. 7, pp. 3456–3470, July 2007 [DOI].
• G. Scutari and S. Barbarossa, “Distributed Space-time Coding for Regenerative Relay Networks,” IEEE Trans. on Wireless Communications, vol. 4, no. 5, pp. 2387–2399, Sept. 2005 [DOI].

• 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].
• X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” Proc. of the 60th Asilomar Conference on Signals, Systems, and Computers, Oct. 25–28, 2026, Pacific Grove, CA, USA.
  Best Student Paper Award (1st Place).
• X. Chen, I. Kuruzov, G. Scutari, and A. Gasnikov, “A Parameter-free Decentralized Algorithm for Composite Convex Optimization,” Proc. of the 2025 IEEE 64th Conference on Decision and Control (CDC25), pp. 1544–1551, Dec. 2025 [DOI].
• M. Maros and G. Scutari, “A Unified View of Decentralized Algorithms for Sparse Linear Regression,” Proc. of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP23), Dec. 10–13, 2023, Los Suenos, Costa Rica.
• J. Xu, Y. Tian, Y. Sun, and G. Scutari, “A Unified Algorithmic Framework for Distributed Composite Optimization,” Proc. of the 59th IEEE Conference on Decision and Control (CDC20), Dec. 14–18, 2020, (online conference because of COVID-19).
• J. Xu, Y. Sun, Y. Tian, and G. Scutari, “A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization,” Proc. of the 2019 IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP19), Dec. 15–18, 2019, Guadeloupe, West Indies.
• A. Daneshmand, G. Scutari, and V. Kungurtsev, “Second Order Guarantees of Gradient Algorithms over Networks,” Proc. of the 56th Annual Allerton Conference on Communication, Control, and Computing, Oct. 3–5, 2018, Monticello, IL, USA.
• 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.
• A. Daneshmand, Y. Sun, G. Scutari, and F. Facchinei, “D2L: Decentralized Dictionary Learning over Dynamic Networks,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
• A. Daneshmand, G. Scutari, and F. Facchinei, “Distributed Dictionary Learning,” Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 6–9, 2016, Pacific Grove, CA, 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.
• P. Di Lorenzo and G. Scutari, “Distributed Nonconvex Optimization Over Time-Varying Networks,” Proc. of the 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP16), March 20–25, 2016, Shanghai, China.
• P. Di Lorenzo and G. Scutari, “Distributed Nonconvex Optimization Over Networks,” Proc. of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pp. 229–232, Dec. 13–16, 2015, Cancun, Mexico.
• S. Sardellitti, G. Scutari, and S. Barbarossa, “Joint Cell Selection and MIMO Precoding in Heterogeneous Networks via Successive Convex Approximation,” Proc. of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP14), pp. 850–854, May 4–9, 2014, Florence, Italy.
• Z. Guan, T. Melodia, and G. Scutari, “Distributed Queueing Games in Interference-limited Wireless Networks,” Proc. of the 2013 IEEE International Conference on Communications (ICC13), pp. 1810–1815, June 9–13, 2013, Budapest, Hungary.
• G. Scutari, D. Palomar, F. Facchinei, and J.-S. Pang, “Distributed Dynamic Pricing for MIMO Interfering Multiuser Systems: A Unified Approach,” Proc. of the 5th International Conference on Network Games, Control and Optimization, Oct. 12–14, 2011, Paris, France.
• 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.
• S. Barbarossa, G. Scutari, and T. Battisti, “Cooperative Sensing for Cognitive Radio Using Decentralized Projection Algorithms,” Proc. of the 10th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC09), pp. 116–120, June 21–24, 2009, Perugia, Italy.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Optimization of Cognitive Radio MIMO Systems via Game Theory,” Proc. of the 1st International Conference on Game Theory for Networks (GameNets 2009), pp. 452–461, May 13–15, 2009, Boğaziçi University, Istanbul, Turkey.
• 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.
• S. Barbarossa, T. Battisti, L. Pescosolido, S. Sardellitti, and G. Scutari, “Distributed Spatial Smoothing Algorithms for Wireless Sensor Networks Having Fast Convergence and Robustness Against Coupling Noise,” Proc. of the 10th IEEE International Symposium on Spread Spectrum Techniques and Applications (ISSSTA08), pp. 7–11, Aug. 25–28, 2008, Bologna, Italy.
• L. Pescosolido, S. Barbarossa, and G. Scutari, “Average Consensus Algorithms Robust Against Channel Noise,” Proc. of the 9th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC08), pp. 261–265, July 7–9, 2008, Recife, Brazil.
• L. Pescosolido, S. Barbarossa, and G. Scutari, “Radar Sensor Networks with Distributed Detection Capabilities,” Proc. of the 2008 IEEE Radar Conference, 5 pages, May 26–30, 2008, Sheraton Golf Parco dei Medici, Rome, Italy.
• A. Fasano and G. Scutari, “The Effect of Additive Noise on Consensus Achievement in Wireless Sensor Networks,” Proc. of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP08), pp. 2277–2280, March 30–April 4, 2008, Las Vegas, NV, USA.
• G. Scutari and S. Barbarossa, “Optimal Distributed Decision over Wireless Sensor Networks Affected by Multipath Fading,” Proc. of the 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP07), pp. 245–248, Dec. 12–14, 2007, St. Thomas, Virgin Islands, USA.
• O. Simeone and G. Scutari, “Pulse-coupled distributed PLLs in Heterogeneous Wireless Networks,” Proc. of the 41st Asilomar Conference on Signals, Systems & Computers, pp. 1770–1775, Nov. 4–7, 2007, Pacific Grove, CA, USA.
• G. Scutari, S. Barbarossa, and L. Pescosolido, “Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels,” Proc. of the 8th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC07), June 17–20, 2007, Helsinki, Finland.
• S. Barbarossa, G. Scutari, and A. Swami, “Achieving Consensus in Self-Organizing Wireless Sensor Networks: The Impact of Network Topology on Energy Consumption,” Proc. of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP07), vol. 2, pp. II-841–II-844, April 15–20, 2007, Honolulu, Hawaii, USA.
• S. Barbarossa, G. Scutari, and A. Swami, “Distributed Detection and Estimation in Decentralized Sensor Networks: An Overview,” Proc. of the 14th European Signal Processing Conference, Sept. 4–8, 2006, Florence, Italy.
• G. Scutari, S. Barbarossa, and L. Pescosolido, “Optimal Decentralized Estimation Through Self-Synchronizing Networks in the Presence of Propagation Delays,” Proc. of the 7th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC06), July 2–5, 2006, Cannes, France.
• 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.
• S. Barbarossa, G. Scutari, and L. Pescosolido, “Global stability of a Population of Mutually Coupled Oscillators Reaching Global Maximum Likelihood Estimate Through a Decentralized Approach,” Proc. of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP06), vol. 4, pp. 385–388, May 14–19, 2006, Toulouse, France.
• L. Pescosolido, S. Barbarossa, and G. Scutari, “Decentralized Detection and Localization Through Sensor Networks Designed as a Population of Self-Synchronizing Oscillators,” Proc. of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP06), vol. 4, pp. 981–984, May 14–19, 2006, Toulouse, France.
• S. Barbarossa, L. Pescosolido, D. Ludovici, and G. Scutari, “Cooperative Wireless Networks based on Distributed Space-time Coding,” Proc. of the 2004 International Workshop on Wireless Ad-Hoc Networks, pp. 30–34, May 31–June 3, 2004, Oulu, Finland.
• G. Scutari, S. Barbarossa, and D. Ludovici, “On the Maximum Achievable Rates in Wireless Meshed Networks: Centralized versus Decentralized Solutions,” Proc. of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP04), vol. 4, pp. 573–576, May 17–21, 2004, Montreal, Quebec, Canada.
• S. Barbarossa and G. Scutari, “Distributed Space-Time Coding Strategies for Wideband Multihop Networks: Regenerative vs. non-Regenerative Relays,” Proc. of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP04), vol. 4, pp. 501–504, May 17–21, 2004, Montreal, Quebec, Canada.
• S. Barbarossa and G. Scutari, “Distributed Space-Time Coding for Multihop Networks,” Proc. of the 2004 IEEE International Conference on Communications (ICC04), vol. 2, pp. 916–920, June 20–24, 2004, Paris, France.
• G. Scutari, S. Barbarossa, and D. Ludovici, “Cooperation Diversity in Multihop Wireless Networks Using Opportunistic Driven Multiple Access,” Proc. of the 4th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC03), pp. 170–174, June 15–18, 2003, Rome, Italy.
• S. Barbarossa and G. Scutari, “Cooperative Diversity Through Virtual Arrays in Multihop Networks,” Proc. of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP03), vol. 4, pp. 209–212, April 6–10, 2003, Hong Kong, China.