On the Convergence of Decentralized Gradient Descent
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Publication:2821798
DOI10.1137/130943170zbMath1345.90068arXiv1310.7063OpenAlexW1616857247MaRDI QIDQ2821798
Kun Yuan, Wotao Yin, Qing Ling
Publication date: 23 September 2016
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1310.7063
Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonlinear programming (90C30)
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Cites Work
- Distributed stochastic subgradient projection algorithms for convex optimization
- Fast linearized Bregman iteration for compressive sensing and sparse denoising
- Error forgetting of Bregman iteration
- Augmented $\ell_1$ and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm
- Fast Distributed Gradient Methods
- Analysis and Generalizations of the Linearized Bregman Method
- Exact Regularization of Convex Programs
- Distributed asynchronous deterministic and stochastic gradient optimization algorithms
- Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
- Decentralized Sparse Signal Recovery for Compressive Sleeping Wireless Sensor Networks
- Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity
- Group-Lasso on Splines for Spectrum Cartography
- Distributed Basis Pursuit
- Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks
- Decentralized Jointly Sparse Optimization by Reweighted $\ell_{q}$ Minimization
- Decentralized Dynamic Optimization Through the Alternating Direction Method of Multipliers
- Consensus and Cooperation in Networked Multi-Agent Systems
- Fastest Mixing Markov Chain on a Graph
- Distributed Subgradient Methods for Multi-Agent Optimization
- Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Convex analysis and monotone operator theory in Hilbert spaces
