Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization
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Zhi-Quan Luo | Peilin Zhao | Congliang Chen | Jiawei Zhang | Li Shen | Z. Luo | P. Zhao | Jiawei Zhang | Li Shen | Congliang Chen
[1] Asuman E. Ozdaglar,et al. Distributed Alternating Direction Method of Multipliers , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
[2] Zhi-Quan Luo,et al. A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimization , 2018, SIAM J. Optim..
[3] Mingyi Hong,et al. Distributed Non-Convex First-Order optimization and Information Processing: Lower Complexity Bounds and Rate Optimal Algorithms , 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.
[4] Laurent Massoulié,et al. Optimal Algorithms for Non-Smooth Distributed Optimization in Networks , 2018, NeurIPS.
[5] Hanlin Tang,et al. Communication Compression for Decentralized Training , 2018, NeurIPS.
[6] Michael W. Mahoney,et al. Sub-Sampled Newton Methods I: Globally Convergent Algorithms , 2016, ArXiv.
[7] Mingyi Hong,et al. Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks , 2017, ICML.
[8] Asuman E. Ozdaglar,et al. Distributed Subgradient Methods for Multi-Agent Optimization , 2009, IEEE Transactions on Automatic Control.
[9] Qing Ling,et al. On the Convergence of Decentralized Gradient Descent , 2013, SIAM J. Optim..
[10] Cong Xu,et al. TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning , 2017, NIPS.
[11] Qing Ling,et al. On the Linear Convergence of the ADMM in Decentralized Consensus Optimization , 2013, IEEE Transactions on Signal Processing.
[12] Tian Ye,et al. DEED: A General Quantization Scheme for Communication Efficiency in Bits , 2020, ArXiv.
[13] Eunho Yang,et al. Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning , 2019, ICML.
[14] Yi Zhou,et al. Communication-efficient algorithms for decentralized and stochastic optimization , 2017, Mathematical Programming.
[15] Yiran Chen,et al. Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.
[16] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[17] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[18] Zhi-Quan Luo,et al. Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[19] Trevor Darrell,et al. Constrained Convolutional Neural Networks for Weakly Supervised Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[20] Ji Liu,et al. DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression , 2019, ICML.
[21] Martin Jaggi,et al. Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication , 2019, ICML.
[22] Xiangfeng Wang,et al. Multi-Agent Distributed Optimization via Inexact Consensus ADMM , 2014, IEEE Transactions on Signal Processing.
[23] Rong Jin,et al. On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization , 2019, ICML.
[24] Wei Zhang,et al. Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent , 2017, NIPS.
[25] Xiangru Lian,et al. D2: Decentralized Training over Decentralized Data , 2018, ICML.
[26] Yair Carmon,et al. Lower bounds for finding stationary points I , 2017, Mathematical Programming.
[27] K. Schittkowski,et al. NONLINEAR PROGRAMMING , 2022 .
[28] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[29] George Michailidis,et al. DAdam: A Consensus-Based Distributed Adaptive Gradient Method for Online Optimization , 2018, IEEE Transactions on Signal Processing.