Full Publication List →

Convergence

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.

Refereed Book Chapters

  • 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).
  • 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].

Journal Papers

  • 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.
  • M. Maros and G. Scutari, “Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence,” Proc. Conference on Neural Information Processing Systems (NeurIPS), 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].
  • K. Slavakis, G. Shetty, L. Cannelli, G. Scutari, U. Nakarmi, and L. Ying, “Kernel Regression Imputation in Manifolds via Bi-linear Modeling: The Dynamic MRI case,” IEEE Trans. on Computational Imaging, vol. 8, pp. 133–147, 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].
  • M. Maros and G. Scutari, “DGD2: A Linearly Convergent Distributed Algorithm for High-dimensional Statistical Recovery,” Proc. Conference on Neural Information Processing Systems (NeurIPS 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].
  • 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].
  • 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. Shetty, K. Slavakis, A. Bose, L. Ying, U. Nakarmi, and G. Scutari, “Bi-Linear Modeling of Data Manifolds for Dynamic-MRI recovery,” IEEE Trans. on Medical Imaging, vol. 39, no. 3, pp. 688–702, March 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. 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].
  • 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).
  • 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).
  • G. Scutari, F. Facchinei, J.-S. Pang, and D. P. Palomar, “Real and Complex Monotone Communication Games,” IEEE Trans. on Information Theory, vol. 60, no. 7, pp. 4197–4231, July 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, “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].
  • 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).
  • G. Scutari, D. P. Palomar, and S. Barbarossa, “The MIMO Iterative Waterfilling Algorithm,” IEEE Trans. on Signal Processing, vol. 57, no. 5, pp. 1917–1935, May 2009 [DOI].
    Top 10 downloaded articles in IEEE TSP (May – July 2009).
  • G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Design of Multiuser MIMO Systems based on Game Theory: A Unified View,” IEEE J. on Selected Areas in Communications: Special Issue on Game Theory, vol. 26, no. 7, pp. 1089–1103, Sept. 2008 [DOI].
  • 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].
  • 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].

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].
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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, F. Facchinei, and J.-S. Pang, “Equilibrium Selection in MIMO Communication Games,” Proc. of the 13th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC12), pp. 80–84, June 17–20, 2012, Cesme, Turkey.
  • G. Scutari, F. Facchinei, J.-S. Pang, and L. Lampariello, “Equilibrium Selection in Power Control Games on the Interference Channel,” Proc. of the 2012 IEEE International Conference on Computer Communications, pp. 675–683, March 25–30, 2012, Orlando, Florida.
  • 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.
  • 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.
  • 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.
  • 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, D. P. Palomar, and S. Barbarossa, “Competitive Design of Multiuser MIMO Interference Systems Based on Game Theory: A Unified Framework,” Proc. of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP08), pp. 5376–5379, March 30–April 4, 2008, Caesars Palace, Las Vegas, Nevada, 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.
  • G. Scutari, D. P. Palomar, and S. Barbarossa, “Distributed Totally Asynchronous Iterative Waterfilling for Wideband Interference Channel with Time/Frequency Offset,” Proc. of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP07), vol. 4, pp. 1325–1328, April 15–20, 2007, Honolulu, Hawaii, USA.
  • G. Scutari, D. P. Palomar, and S. Barbarossa, “Asynchronous Iterative Waterfilling for Gaussian Frequency-Selective Interference Channels: A Unified Framework,” Proc. of the 2007 Information Theory and Applications Workshop (ITA07), pp. 349–358, Jan. 29–Feb. 2, 2007, La Jolla, CA, USA.
  • G. Scutari, D. P. Palomar, and S. Barbarossa, “Simultaneous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels,” Proc. of the 2006 IEEE International Symposium on Information Theory (ISIT06), pp. 600–604, July 9–14, 2006, Seattle, WA, USA.
  • 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.