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Guannan Qu
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Cited by
Year
Harnessing smoothness to accelerate distributed optimization
G Qu, N Li
IEEE Transactions on Control of Network Systems 5 (3), 1245-1260, 2017
4042017
Accelerated distributed Nesterov gradient descent
G Qu, N Li
IEEE Transactions on Automatic Control 65 (6), 2566-2581, 2019
167*2019
Real-time decentralized voltage control in distribution networks
N Li, G Qu, M Dahleh
2014 52nd Annual Allerton Conference on Communication, Control, and …, 2014
1052014
On the exponential stability of primal-dual gradient dynamics
G Qu, N Li
IEEE Control Systems Letters 3 (1), 43-48, 2018
882018
Optimal scheduling of battery charging station serving electric vehicles based on battery swapping
X Tan, G Qu, B Sun, N Li, DHK Tsang
IEEE Transactions on Smart Grid 10 (2), 1372-1384, 2017
872017
A random forest method for real-time price forecasting in New York electricity market
J Mei, D He, R Harley, T Habetler, G Qu
2014 IEEE PES General Meeting| Conference & Exposition, 1-5, 2014
712014
Optimal distributed feedback voltage control under limited reactive power
G Qu, N Li
IEEE Transactions on Power Systems 35 (1), 315-331, 2019
692019
Online optimization with predictions and switching costs: Fast algorithms and the fundamental limit
Y Li, G Qu, N Li
IEEE Transactions on Automatic Control 66 (10), 4761-4768, 2020
66*2020
Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions
G Qu, D Brown, N Li
Automatica 105, 206-215, 2019
52*2019
Finite-Time Analysis of Asynchronous Stochastic Approximation and -Learning
G Qu, A Wierman
Conference on Learning Theory, 3185-3205, 2020
512020
Scalable reinforcement learning of localized policies for multi-agent networked systems
G Qu, A Wierman, N Li
Learning for Dynamics and Control, 256-266, 2020
382020
Distributed optimal voltage control with asynchronous and delayed communication
S Magnússon, G Qu, N Li
IEEE Transactions on Smart Grid 11 (4), 3469-3482, 2020
31*2020
Learning optimal power flow: Worst-case guarantees for neural networks
A Venzke, G Qu, S Low, S Chatzivasileiadis
2020 IEEE International Conference on Communications, Control, and Computing …, 2020
262020
Scalable multi-agent reinforcement learning for networked systems with average reward
G Qu, Y Lin, A Wierman, N Li
Advances in Neural Information Processing Systems 33, 2074-2086, 2020
252020
Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision
X Chen, G Qu, Y Tang, S Low, N Li
arXiv preprint arXiv:2102.01168, 2021
222021
Multi-agent reinforcement learning in stochastic networked systems
Y Lin, G Qu, L Huang, A Wierman
Advances in Neural Information Processing Systems 34, 2021
20*2021
Voltage control using limited communication
S Magnússon, G Qu, C Fischione, N Li
IEEE Transactions on Control of Network Systems 6 (3), 993-1003, 2019
202019
Exploiting fast decaying and locality in multi-agent mdp with tree dependence structure
G Qu, N Li
2019 IEEE 58th conference on decision and control (CDC), 6479-6486, 2019
162019
Short-term wind power forecasting based on numerical weather prediction adjustment
G Qu, J Mei, D He
2013 11th IEEE International Conference on Industrial Informatics (INDIN …, 2013
122013
Combining model-based and model-free methods for nonlinear control: A provably convergent policy gradient approach
G Qu, C Yu, S Low, A Wierman
arXiv preprint arXiv:2006.07476, 2020
112020
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Articles 1–20