Distributed Signal Processing and Optimization Based on In-Network Subspace Projections
暂无分享,去创建一个
Sergio Barbarossa | Stefania Sardellitti | Paolo Di Lorenzo | S. Barbarossa | S. Sardellitti | P. Di Lorenzo
[1] Sergio Barbarossa,et al. Distributed Detection and Estimation in Wireless Sensor Networks , 2013, ArXiv.
[2] Sergio Barbarossa,et al. Distributed Signal Recovery Based on In-network Subspace Projections , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[3] Angelia Nedic,et al. A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks , 2018, 2020 Information Theory and Applications Workshop (ITA).
[4] S. Kay. Fundamentals of statistical signal processing: estimation theory , 1993 .
[5] Zoran Obradovic,et al. Boosting Algorithms for Parallel and Distributed Learning , 2022 .
[6] Geert Leus,et al. Advances in Distributed Graph Filtering , 2019, IEEE Transactions on Signal Processing.
[7] Vivek K Goyal,et al. Foundations of Signal Processing , 2014 .
[8] Sergio Barbarossa,et al. Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks , 2014, IEEE Signal Processing Magazine.
[9] Gesualdo Scutari,et al. Distributed nonconvex constrained optimization over time-varying digraphs , 2018, Mathematical Programming.
[10] L. Grippo,et al. Exact penalty functions in constrained optimization , 1989 .
[11] Angelia Nedic,et al. Distributed optimization over time-varying directed graphs , 2013, 52nd IEEE Conference on Decision and Control.
[12] Gesualdo Scutari,et al. NEXT: In-Network Nonconvex Optimization , 2016, IEEE Transactions on Signal and Information Processing over Networks.
[13] Asuman E. Ozdaglar,et al. Distributed Subgradient Methods for Multi-Agent Optimization , 2009, IEEE Transactions on Automatic Control.
[14] Soummya Kar,et al. Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication , 2008, IEEE Transactions on Information Theory.
[15] Santiago Segarra,et al. Optimal Graph-Filter Design and Applications to Distributed Linear Network Operators , 2017, IEEE Transactions on Signal Processing.
[16] S. Haykin. Adaptive Filters , 2007 .
[17] Soummya Kar,et al. Annealing for Distributed Global Optimization , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).
[18] Ali H. Sayed,et al. Diffusion LMS Strategies for Distributed Estimation , 2010, IEEE Transactions on Signal Processing.
[19] Wotao Yin,et al. On Nonconvex Decentralized Gradient Descent , 2016, IEEE Transactions on Signal Processing.
[20] B. Logan,et al. Signal recovery and the large sieve , 1992 .
[21] Ali H. Sayed,et al. Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks , 2011, IEEE Transactions on Signal Processing.
[22] Francisco Facchinei,et al. Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization , 2014, ArXiv.
[23] Dennis S. Bernstein,et al. Matrix Mathematics: Theory, Facts, and Formulas with Application to Linear Systems Theory , 2005 .
[24] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[25] Pascal Bianchi,et al. Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization , 2011, IEEE Transactions on Automatic Control.
[26] Ali H. Sayed,et al. Distributed Inference over Networks under Subspace Constraints , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[27] José M. F. Moura,et al. Fast Distributed Gradient Methods , 2011, IEEE Transactions on Automatic Control.
[28] Sergio Barbarossa,et al. Distributed signal subspace projection algorithms with maximum convergence rate for sensor networks with topological constraints , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[29] Ali H. Sayed,et al. Sparse Distributed Learning Based on Diffusion Adaptation , 2012, IEEE Transactions on Signal Processing.
[30] Stephen P. Boyd,et al. Fastest Mixing Markov Chain on a Graph , 2004, SIAM Rev..
[31] Vyacheslav Kungurtsev,et al. Second-order Guarantees of Distributed Gradient Algorithms , 2018, SIAM J. Optim..
[32] Asuman E. Ozdaglar,et al. Constrained Consensus and Optimization in Multi-Agent Networks , 2008, IEEE Transactions on Automatic Control.
[33] Sergio Barbarossa,et al. Cooperative sensing for cognitive radio using decentralized projection algorithms , 2009, 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications.
[34] John N. Tsitsiklis,et al. Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms , 1984, 1984 American Control Conference.
[35] S. R,et al. Data Mining with Big Data , 2017, 2017 11th International Conference on Intelligent Systems and Control (ISCO).
[36] Qing Ling,et al. EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization , 2014, 1404.6264.
[37] Sergio Barbarossa,et al. Distributed Adaptive Learning of Graph Signals , 2016, IEEE Transactions on Signal Processing.
[38] Minghui Zhu,et al. An approximate dual subgradient algorithm for distributed non-convex constrained optimization , 2010 .
[39] Gonzalo Mateos,et al. Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge , 2014, IEEE Signal Processing Magazine.
[40] Baltasar Beferull-Lozano,et al. Fast Distributed Subspace Projection via Graph Filters , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[41] Mingyi Hong,et al. Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks , 2017, ICML.
[42] Ali Sayed,et al. Adaptation, Learning, and Optimization over Networks , 2014, Found. Trends Mach. Learn..
[43] Qing Ling,et al. Decentralized learning for wireless communications and networking , 2015, ArXiv.
[44] Ali H. Sayed,et al. Adaptation and Learning Over Networks Under Subspace Constraints—Part II: Performance Analysis , 2019, IEEE Transactions on Signal Processing.
[45] José M. F. Moura,et al. Discrete Signal Processing on Graphs , 2012, IEEE Transactions on Signal Processing.
[46] H. Vincent Poor,et al. Distributed learning in wireless sensor networks , 2005, IEEE Signal Processing Magazine.
[47] John A. Stankovic,et al. Research Directions for the Internet of Things , 2014, IEEE Internet of Things Journal.
[48] Sergio Barbarossa,et al. Signals on Graphs: Uncertainty Principle and Sampling , 2015, IEEE Transactions on Signal Processing.
[49] Ali H. Sayed,et al. Distributed Learning in Non-Convex Environments— Part II: Polynomial Escape From Saddle-Points , 2019, IEEE Transactions on Signal Processing.
[50] Ran Wolff,et al. Distributed Data Mining in Peer-to-Peer Networks , 2006, IEEE Internet Computing.
[51] John N. Tsitsiklis,et al. Gradient Convergence in Gradient methods with Errors , 1999, SIAM J. Optim..
[52] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[53] Damiano Varagnolo,et al. Newton-Raphson Consensus for Distributed Convex Optimization , 2015, IEEE Transactions on Automatic Control.
[54] Ali H. Sayed,et al. Adaptation and Learning Over Networks Under Subspace Constraints—Part II: Performance Analysis , 2019, IEEE Transactions on Signal Processing.
[55] D. Donoho,et al. Uncertainty principles and signal recovery , 1989 .
[56] Martin J. Wainwright,et al. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling , 2010, IEEE Transactions on Automatic Control.
[57] Georgios B. Giannakis,et al. Consensus-Based Distributed Support Vector Machines , 2010, J. Mach. Learn. Res..