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Rates and complexity

Preprints

  • X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” preprint, arXiv:2605.00711, 2026.

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.

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.
  • 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].
  • 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].
  • 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].
  • 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).

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).
  • 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.
  • 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).
  • C.-S. Lee, N. Michelusi, and G. Scutari, “Distributed Quantized Weight-Balancing and Average Consensus over Digraphs,” Proc. of the 57th IEEE Conference on Decision and Control (CDC18), Dec. 17–19, 2018, Fontainebleau, Miami Beach, FL, USA.
  • 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.
  • 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.
  • 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.
  • 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.