Full Publication List →

Optimization

Preprints

  • K. Ding and G. Scutari, “From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing,” preprint, arXiv:2610.03709, 2026.
  • K. Ding, M. Maros, and G. Scutari, “A New Decomposition Paradigm for Graph-structured Nonlinear Programs via Message Passing,” preprint, arXiv:2512.24676, 2025.
  • 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.

Monograph (Lecture Notes)

  • A. Nedić, J.-S. Pang, G. Scutari, and Y. Sun, Multi-agent Optimization. Lecture Notes in Mathematics, Springer Verlag, CIME Subseries, 2018. [link]

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].
  • 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.

Magazine Papers

  • 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).

Journal Papers

  • 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.
  • 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].
  • 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].
  • D. Kovalev, A. Beznosikov, E. Borodich, A. Gasnikov, and G. Scutari, “Optimal Gradient Sliding and Its Application to Optimal Distributed Optimization under Similarity,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 (Spotlight, acceptance rate 4%).
  • 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.
  • 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].
  • 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.
  • 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].
  • A. Daneshmand, G. Scutari, and V. Kungurtsev, “Second-order Guarantees of Distributed Gradient Algorithms,” SIAM J. on Optimization, vol. 30, no. 4, pp. 3029–3068, 2020 [DOI].
  • Y. Tian, Y. Sun, and G. Scutari, “Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization,” IEEE Trans. on Automatic Control, vol. 65, no. 12, pp. 5264–5279, 2020 [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).
  • A. Al-Shuwaili, O. Simeone, A. Bagheri, and G. Scutari, “Joint Uplink/Downlink Optimization for Backhaul-Limited Mobile Cloud Computing with User Scheduling,” IEEE Trans. on Signal and Information Processing over Networks, vol. 3, no. 4, pp. 787–802, Dec. 2017 [DOI].
    Top 50 downloaded articles in IEEE TSP (Feb. 2017–June 2017).
  • 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).
  • 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].
  • A. Daneshmand, F. Facchinei, V. Kungurtsev, and G. Scutari, “Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 15, pp. 3914–3929, August 2015 [DOI].
    Top 50 downloaded articles in IEEE TSP (July 2015).
  • S. Sardellitti, G. Scutari, and S. Barbarossa, “Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing,” IEEE Trans. on Signal and Information Processing over Networks, vol. 1, no. 2, pp. 89–103, June 2015 [DOI].
    Top 50 downloaded articles in IEEE TSIPN (Dec. 2016–June 2017).
    Highly cited paper (ISI Web of Knowledge, 2016–2019).
    2020 IEEE Signal Processing Society Best Paper Award.
  • F. Facchinei, G. Scutari, and S. Sagratella, “Parallel Selective Algorithms for Big Data Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 7, pp. 1874–1889, April 2015 [DOI].
    Top 10 downloaded articles in IEEE TSP (March 2015)
    Top 50 downloaded articles in IEEE TSP (April 2015, May 2015).
  • 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].
  • G. Scutari, F. Facchinei, P. Song, D. P. Palomar, and J.-S. Pang, “Decomposition by Partial Linearization: Parallel Optimization of Multi-agent Systems,” IEEE Trans. on Signal Processing, vol. 62, no. 3, pp. 641–656, Feb. 2014 [DOI].
    2015 IEEE Signal Processing Society Young Author Best Paper Award.
    Highly cited paper (ISI Web of Knowledge, 2016, 2017, 2018, 2019, 2020).
  • Y. Yang, F. Rubio, G. Scutari, and D. P. Palomar, “Multi-Portfolio Optimization: A Potential Game Approach,” IEEE Trans. on Signal Processing, vol. 61, no. 22, pp. 5590–5602, Nov. 2013 [DOI].
    Top 10 downloaded articles in IEEE TSP (Oct. 2013).
  • 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].
  • I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “Demand-Side Management via Distributed Energy Generation and Storage Optimization,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 866–876, June 2013 [DOI].
    Top 50 downloaded articles in IEEE TSG (Jan. 2017, April 2017).
    Highly cited paper (ISI Web of Knowledge, 2016, 2017, 2018, 2019, 2020).
  • I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. 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 [DOI].
    Top 10 downloaded articles in IEEE TSP (June, July 2013).
  • 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].

Refereed Conference Papers

  • 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].
  • A. Agafonov, P. Dvurechensky, G. Scutari, A. Gasnikov, D. Kamzolov, A. Lukashevich, and A. Daneshmand, “An Accelerated Second-Order Method for Distributed Stochastic Optimization,” Proc. of the 60th IEEE Conference on Decision and Control (CDC), 14–17 Dec. 2021, Austin, TX, USA.
  • 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.
  • Y. Tian, Y. Sun, G. Scutari, and B. Du, “ASY-SONATA: Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization,” Proc. of the 56th Allerton Conference on Communication, Control, and Computing (Allerton 2018), Oct. 2–5, 2018, Monticello, IL, USA.
  • 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.
  • I. Notarnicola, Y. Sun, G. Scutari, and G. Notarstefano, “Distributed Big-Data Optimization via Block Communications,” Proc. of the 2017 IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP17), Dec. 10–13, 2017, Curaçao, Dutch Antilles.
    Best Student Paper Award.
  • L. Cannelli, F. Facchinei, V. Kungurtsev, and G. Scutari, “Essentially Cyclic Asynchronous Nonconvex Large-Scale Optimization,” Proc. of the 2017 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2017), July 3–6, 2017, Sapporo, Japan.
    Best Paper Award runner-up.
  • 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. Mokhtari, A. Koppel, G. Scutari, and A. Ribeiro, “Large-scale Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
  • L. Cannelli, F. Facchinei, V. Kungurtsev, and G. Scutari, “Asynchronous Parallel Nonconvex Large-scale Optimization,” 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.
  • L. Cannelli, G. Scutari, F. Facchinei, and V. Kungurtsev, “Parallel Asynchronous Lock-Free Algorithms for Nonconvex Big-Data Optimization,” Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 6–9, 2016, Pacific Grove, CA, USA.
    Best Paper Award runner-up (track D). [here]
  • 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, S. Barbarossa, and G. Scutari, “Distributed Mobile Cloud Computing: Joint Optimization of Radio and Computational Resources,” Proc. of the 2014 IEEE Global Communications Conference, pp. 1505–1510, Dec. 8–12, 2014, Austin, TX, USA.
  • A. Daneshmand, F. Facchinei, V. Kungurtsev, and G. Scutari, “Flexible Selective Parallel Algorithms for Big Data Optimization,” Proc. of the 48th Asilomar Conference on Signals, Systems, and Computers, pp. 3–7, Nov. 2–5, 2014, Pacific Grove, CA, USA.
  • S. Sardellitti, G. Scutari, and S. Barbarossa, “Distributed Joint Optimization of Radio and Computational Resources for Mobile Cloud Computing,” Proc. of the 3rd IEEE International Conference on Cloud Networking, pp. 211–216, Oct. 8–10, 2014, Luxembourg.
  • F. Facchinei, S. Sagratella, and G. Scutari, “Flexible Parallel Algorithms for Big Data Optimization,” Proc. of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP14), pp. 7208–7212, May 4–9, 2014, Florence, Italy.
  • 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.
  • Y. Yang, G. Scutari, and D. Palomar, “Parallel Stochastic Decomposition Algorithms for Multi-Agent Systems,” Proc. of the 14th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC13), pp. 180–184, June 16–19, 2013, Darmstadt, Germany.
  • G. Scutari, F. Facchinei, P. Song, D. Palomar, and J.-S. Pang, “Decomposition by Partial Linearization in Multiuser Systems,” Proc. of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP13), pp. 4424–4428, May 26–31, 2013, Vancouver, Canada.
  • I. Atzeni, L. Ordonez, G. Scutari, D. Palomar, and J. Fonollosa, “Cooperative Day-Ahead Bidding Strategies for Demand-side Expected Cost Minimization,” Proc. of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP13), pp. 5224–5228, May 26–31, 2013, Vancouver, Canada.
  • I. Atzeni, L. Ordonez, G. Scutari, D. Palomar, and J. Fonollosa, “Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization,” Proc. of the 2012 IEEE Smart Grid Communications, pp. 91–96, Nov. 5–8, 2012, Tainan, Taiwan.
  • 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.
  • Y. Yang, F. Rubio, G. Scutari, and D. Palomar, “Multi-Portfolio Optimization: A Potential Game Approach,” Proc. of the 2nd International ICST Conference on Game Theory for Networks, pp. 1–5, April 16–18, 2011, Shanghai, China.
  • S. Barbarossa, S. Sardellitti, and G. Scutari, “Joint Optimization of Detection Thresholds and Power Allocation for Opportunistic Access in Multicarrier Cognitive Radio Networks,” Proc. of the Third IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP09), pp. 404–407, Dec. 13–16, 2009, Radisson Aruba Resort, Casino & Spa Aruba, Dutch Antilles.
  • 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.
  • 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.
  • 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, “Optimal Rate Allocation for Multiple Access Wideband Systems with Rate Profile Constraint and Adaptive Power Control,” Proc. of the 4th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC03), pp. 115–119, June 15–18, 2003, Rome, Italy.