Yujie Tang
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Publications

Preprints

  1. Yingpeng Duan and Yujie Tang. “Zeroth-order feedback optimization in multi-agent systems: Tackling coupled constraints.” [link]

  2. Huaiyi Mu, Yujie Tang and Zhongkui Li. “Variance-reduced gradient estimator for nonconvex zeroth-order distributed optimization.” [arXiv]

  3. Silan Zhang and Yujie Tang. “Zeroth-order Katyusha: An accelerated derivative-free method for composite convex optimization,” to appear in the 63th IEEE Conference on Decision and Control (CDC 2024) [arXiv]

  4. Yang Zheng, Chih-fan Pai and Yujie Tang. “Benign nonconvex landscapes in optimal and robust control, Part II: Extended Convex Lifting.” [arXiv]

  5. Yang Zheng, Chih-fan Pai and Yujie Tang. “Benign nonconvex landscapes in optimal and robust control, Part I: Global optimality.” [arXiv]

  6. Ruiyang Jin, Yujie Tang and Jie Song. “Zeroth-order feedback-based optimization for distributed demand response.” [arXiv]

Journal Articles

  1. Yize Li, Chao Lu, Yujie Tang, Chen Fang, Yong Cui. “Dynamic control and time-delayed channel scheduling co-design for voltage control in active distribution networks,” IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1837–1848, 2024.

  2. Yujie Tang, Yang Zheng and Na Li. “Analysis of the optimization landscape of Linear Quadratic Gaussian (LQG) control,” Mathematical Programming, vol. 202, pp. 399–444, 2023. [arXiv]

  3. Tianpeng Zhang, Victor Qin, Yujie Tang and Na Li. “Distributed information-based source seeking,” IEEE Transactions on Robotics, vol. 39, no. 6, pp. 4749–4767, 2023. [arXiv]

  4. Yujie Tang, Zhaolin Ren and Na Li. “Zeroth-order feedback optimization for cooperative multi-agent systems,” Automatica, vol. 148, 110741, 2023. [arXiv]

  5. Yujie Tang, Emiliano Dall’Anese, Andrey Bernstein and Steven Low. “Running primal-dual gradient method for time-varying nonconvex problems,” SIAM Journal on Control and Optimization, vol. 60, no. 4, pp. 1970–1990, 2022. [arXiv]

  6. Xin Chen, Guannan Qu, Yujie Tang, Steven Low and Na Li. “Reinforcement learning for selective key applications in power systems: Recent advances and future challenges,” IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2935–2958, 2022.

  7. Yujie Tang, Vikram Ramanathan, Junshan Zhang and Na Li. “Communication-efficient distributed SGD with compressed sensing,” IEEE Control Systems Letters, vol. 6, pp. 2054–2059, 2021.

  8. Yingying Li, Yujie Tang, Runyu Zhang and Na Li. “Distributed reinforcement learning for decentralized linear quadratic control: A derivative-free policy optimization approach,” IEEE Transactions on Automatic Control, vol. 67, no. 12, pp. 6429–6444, 2022. [arXiv]

  9. Yujie Tang, Junshan Zhang and Na Li. “Distributed zero-order algorithms for nonconvex multi-agent optimization,” IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 269–281, Mar. 2021. [arXiv]

  10. Yujie Tang, Guannan Qu and Na Li. “Semi-global exponential stability of primal-dual gradient dynamics for constrained convex optimization,” Systems & Control Letters, vol. 144, Oct. 2020. [arXiv]

  11. Yujie Tang, Krishnamurthy Dvijotham and Steven Low. “Real-time optimal power flow,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2963–2973, Nov. 2017.

  12. Yujie Tang and Steven H. Low. “Optimal placement of energy storage in distribution networks,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 3094–-3103, Nov. 2017.

  13. Qiuyu Peng, Yujie Tang and Steven Low. “Feeder reconfiguration in distribution networks based on convex relaxation of OPF,” IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 1793–1804, Jul. 2015.

  14. Yujie Tang, Laming Chen and Yuantao Gu. “On the performance bound of sparse estimation with sensing matrix perturbation,” IEEE Transactions on Signal Processing, vol. 61, no. 17, pp. 4372–-4386, Sep. 2013.

Conference Papers

  1. Yujie Tang and Yang Zheng. “On the global optimality of direct policy search for nonsmooth \(\mathcal{H}_\infty\) output-feedback control,” in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), pp. 6148–6153, 2023. [arXiv]

  2. Zhaolin Ren, Yujie Tang and Na Li. “Escaping saddle points in zeroth-order optimization: the power of two-point estimators,” in Proceedings of the 40th International Conference on Machine Learning, pp. 28914–28975, 2023. [link]

  3. Xin Chen, Yujie Tang and Na Li. “Improve single-point zeroth-order optimization using high-pass and low-pass filters,” in Proceedings of the 39th International Conference on Machine Learning, pp. 3603–3620, 2022.

  4. Tianpeng Zhang, Victor Qin, Yujie Tang and Na Li. “Source seeking by dynamic source location estimation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2598–2605, 2021.

  5. Yujie Yang, Yang Zheng and Na Li. “Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, pp. 599–-610, 2021.

  6. Yujie Tang, Zhaolin Ren and Na Li. “Zeroth-order feedback optimization for cooperative multi-agent systems,” in Proceedings of the 59th IEEE Conference on Decision and Control, pp. 3649–-3656, 2020.

  7. Yujie Tang and Steven Low. “A second-order saddle point method for time-varying optimization,” in Proceedings of the 58th IEEE Conference on Decision and Control, pp. 3928–-3935, 2019.

  8. Yujie Tang and Na Li. “Distributed zero-order algorithms for nonconvex multi-agent optimization,” in Proceedings of the 57th Annual Allerton Conference on Communication, Control, and Computing, pp. 781–-786, 2019.

  9. Yujie Tang, Emiliano Dall’Anese, Andrey Bernstein and Steven H. Low. “A feedback-based regularized primal-dual gradient method for time-varying nonconvex optimization,” in Proceedings of the 57th IEEE Conference on Decision and Control, pp. 3244–-3250, 2018.

  10. Yujie Tang and Steven Low. “Distributed algorithm for time-varying optimal power flow,” in Proceedings of the 56th IEEE Conference on Decision and Control, pp. 3264–-3270, 2017.

  11. Yujie Tang and Steven H. Low. “Optimal placement of energy storage in distribution networks,” in Proceedings of the 55th IEEE Conference on Decision and Control, pp. 3258–-3264, 2016.