邓琪，现任上海交通大学安泰经济与管理学院副教授，此前在上海财经大学信息管理与工程学院工作，分别担任助理教授和副教授。分别于美国佛罗里达大学和上海交通大学获得计算机专业博士和学士学位。主要研究兴趣包括数学规划和机器学习。近年来的研究成果发表在 Math Programming, IJOC, POMS, NeurIPS, ICML 等管理、优化与机器学习领域的期刊和会议上。 

研究兴趣:  随机、非光滑与约束优化的算法与复杂度理论; 联合预测与优化的集成模型；能源与电力系统中的应用

近期相关工作:

Lin, Z. & Deng, Q. (2024) Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints. NeurIPS 2024

Deng, Q., Feng, Q., Gao, W., Ge, D., Jiang, B., Jiang, Y., Liu, J., Liu, T., Xue, C., & Ye, Y. (2024). An Enhanced ADMM-based Interior Point Method for Linear and Conic Optimization. Informs Journal on Computing.

Lin, Z., Xue, C., Deng, Q., & Ye, Y. (2024). A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes. ICML 2024. ing

Processes

Gao, W., & Deng, Q. (2024). Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity. ICML 2024

Tan, J., Xue, C., Zhang, C., Deng, Q., Ge, D., & Ye, Y. (2024). A Homogenization Approach for Gradient-Dominated Stochastic Optimization.UAI 2024. 

Xie, C., Li, C., Zhang, C., Deng, Q., Ge, D., & Ye, Y. (2024). Trust region methods for nonconvex stochastic optimization beyond Lipschitz smoothness. Proceedings of the AAAI Conference on Artificial Intelligence, 

Liu, J., Xie, C., Deng, Q., Ge, D., & Ye, Y. (2024). Sketched Newton Value Iteration for Large-Scale Markov Decision Processes. Proceedings of the AAAI Conference on Artificial Intelligence, 

Lin, Z., Xia, J., Deng, Q., & Luo, L. (2024). Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 

Han, Q., Li, C., Lin, Z., Chen, C., Deng, Q., Ge, D., Liu, H., & Ye, Y. (2024). A Low-Rank ADMM Splitting Approach for Semidefinite Programming. arXiv preprint arXiv:2403.09133. 

Gao, W., & Deng, Q. (2024). Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization. Advances in Neural Information Processing Systems, 36. 

Boob, D., Deng, Q., & Lan, G. (2024). Level constrained first order methods for function constrained optimization. Mathematical programming, 1-61. 

Shi, Z., Wang, X., Yan, D., Chen, S., Lin, Z., Xia, J., & Deng, Q. (2023). An accelerated primal‐dual method for semi‐definite programming relaxation of optimal power flow. IET Energy Systems Integration, 5(4), 477-490. 

Liu, J., Xie, C., Deng, Q., Ge, D., & Ye, Y. (2023). Stochastic Dimension-reduced Second-order Methods for Policy Optimization. arXiv preprint arXiv:2301.12174. 

Lin, Z., & Deng, Q. (2023). Gbm-based bregman proximal algorithms for constrained learning. arXiv preprint arXiv:2308.10767. 

Boob, D., Deng, Q., & Lan, G. (2023). Stochastic first-order methods for convex and nonconvex functional constrained optimization. Mathematical programming, 197(1), 215-279. 

Boob, D., & Deng, Q. (2023). First-order methods for Stochastic Variational Inequality problems with Function Constraints. arXiv preprint arXiv:2304.04778. 

Deng, Q., & Gao, W. (2021). Minibatch and momentum model-based methods for stochastic weakly convex optimization. Advances in Neural Information Processing Systems, 34, 23115-23127. 

邓琪, 高建军, 葛冬冬, 何斯迈, 江波, 李晓澄, 王子卓, 杨超林, & 叶荫宇. (2020). 现代优化理论与应用. 中国科学: 数学, 50(7), 899-968. 

Deng, Q., & Lan, C. (2020). Efficiency of coordinate descent methods for structured nonconvex optimization. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 

Boob, D., Deng, Q., Lan, G., & Wang, Y. (2020). A feasible level proximal point method for nonconvex sparse constrained optimization. Advances in Neural Information Processing Systems, 33, 16773-16784. 

Tan, Y., Paul, A. A., Deng, Q., & Wei, L. (2017). Mitigating inventory overstocking: Optimal order‐up‐to level to achieve a target fill rate over a finite horizon. Production and Operations Management, 26(11), 1971-1988. 

数据结构 (计划)