Articles with public access mandates - Shi Pu 濮实Learn more
Available somewhere: 15
Push–pull gradient methods for distributed optimization in networks
S Pu, W Shi, J Xu, A Nedić
IEEE Transactions on Automatic Control 66 (1), 1-16, 2020
Mandates: US National Science Foundation, US Department of Defense
Distributed stochastic gradient tracking methods
S Pu, A Nedić
Mathematical Programming 187 (1), 409-457, 2021
Mandates: US National Science Foundation, US Department of Defense
A general framework for decentralized optimization with first-order methods
R Xin, S Pu, A Nedić, UA Khan
Proceedings of the IEEE 108 (11), 1869-1889, 2020
Mandates: US National Science Foundation
A sharp estimate on the transient time of distributed stochastic gradient descent
S Pu, A Olshevsky, IC Paschalidis
IEEE Transactions on Automatic Control, 2021
Mandates: US National Science Foundation, US Department of Energy, US Department of …
Asymptotic network independence in distributed stochastic optimization for machine learning: Examining distributed and centralized stochastic gradient descent
S Pu, A Olshevsky, IC Paschalidis
IEEE signal processing magazine 37 (3), 114-122, 2020
Mandates: US National Science Foundation, US Department of Defense, US National …
A Compressed Gradient Tracking Method for Decentralized Optimization with Linear Convergence
Y Liao, Z Li, K Huang, S Pu
IEEE Transactions on Automatic Control, 2022
Mandates: National Natural Science Foundation of China
Compressed gradient tracking for decentralized optimization over general directed networks
Z Song, L Shi, S Pu, M Yan
IEEE Transactions on Signal Processing, 2022
Mandates: US National Science Foundation, National Natural Science Foundation of China
Improving the transient times for distributed stochastic gradient methods
K Huang, S Pu
IEEE Transactions on Automatic Control, 2022
Mandates: National Natural Science Foundation of China
A flocking-based approach for distributed stochastic optimization
S Pu, A Garcia
Operations Research 66 (1), 267-281, 2018
Mandates: US National Science Foundation, US Department of Defense
Swarming for faster convergence in stochastic optimization
S Pu, A Garcia
SIAM Journal on Control and Optimization 56 (4), 2997-3020, 2018
Mandates: US National Science Foundation, US Department of Defense
Distributed random reshuffling over networks
K Huang, X Li, A Milzarek, S Pu, J Qiu
IEEE Transactions on Signal Processing, 2023
Mandates: National Natural Science Foundation of China
A linearly convergent robust compressed push-pull method for decentralized optimization
Y Liao, Z Li, S Pu
2023 62nd IEEE Conference on Decision and Control (CDC), 4156-4161, 2023
Mandates: National Natural Science Foundation of China
An Online Mechanism for Resource Allocation in Networks
S Pu, JJ Escudero-Garzás, A Garcia, S Shahrampour
IEEE Transactions on Control of Network Systems 7 (3), 1140-1150, 2020
Mandates: US National Science Foundation
Private and accurate Decentralized optimization via encrypted and structured functional perturbation
Y Zhou, S Pu
IEEE Control Systems Letters 7, 1339-1344, 2022
Mandates: National Natural Science Foundation of China
Erratum: Swarming for Faster Convergence in Stochastic Optimization
S Pu, A Garcia
SIAM Journal on Control and Optimization 56 (6), 4488-4492, 2018
Mandates: US National Science Foundation, US Department of Defense
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