[8] Shenglong Zhou, Shuai Li, Hui Zhang, and Ziyan Luo, Sharp-peak functions for exactly penalizing binary integer programming, 2025. RG, ArXiv

[7] Shenglong Zhou, Lili Pan, Naihua Xiu, and Geoffrey Ye Li, A 0/1 constrained optimization solving sample average approximation for chance constrained programming, Mathematics of Operations Research, 50, 4, 2688-2716, 2024. RG, ArXiv, Code

[6] Shenglong Zhou, Ziyan Luo, Naihua Xiu, and Geoffrey Ye Li, Computing one-bit compressive sensing via double-sparsity constrained optimization, IEEE Transactions on Signal Processing, 70, 1593-1608, 2022. RG, ArXiv, Code

[5] Shenglong Zhou, Lili Pan, Naihua Xiu, and Huoduo Qi, Quadratic convergence of smoothing Newton's method for 0/1 loss optimization, SIAM Journal on Optimization, 31, 3184–3211, 2021. RG, ArXiv, Code

[4] Huajun Wang, Yuanhai Shao, Shenglong Zhou, Ce Zhang, and Naihua Xiu, Support vector machine classifier via L0/1 soft-margin loss, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 7253-7265, 2022. RG, ArXiv, Code

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[3] Dongrui Wang, Naihua Xiu, Shenglong Zhou, Optimality conditions for double-sparsity constrained optimization, Advances in Mathematics (China), 53(6), 1-13, 2024.

[2] Hui Zhang, Shenglong Zhou, Geoffrey Ye Li, Naihua Xiu, and Yiju Wang, A step function based recursion method for 0/1 deep neural networks, Applied Mathematics and Computation, 488, 1-16, 2025. RG, ArXiv

[1] Shenglong Zhou, Lili Pan, and Naihua Xiu, Heaviside set constrained optimization: optimality and Newton method, 2020. RG, ArXiv

[20] Shenglong Zhou, Gradient projection newton pursuit for sparsity constrained optimization, Applied and Computational Harmonic Analysis, 61, 75-100, 2022. RG, ArXiv, Code

[19] Shenglong Zhou, Sparse SVM for sufficient data reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 5560-5571, 2022. RG, ArXiv, Code

[18] Shenglong Zhou, Naihua Xiu, and Huoduo Qi, Global and quadratic convergence of Newton hard-thresholding pursuit, Journal of Machine Learning Research, 22, 1−45, 2021. RG, ArXiv, Code

[17] Shenglong Zhou, Lili Pan, Mu Li, and Meijuan Shang, Newton hard-thresholding pursuit for sparse linear complementarity problem via a new merit function, SIAM Journal on Scientific Computing, 43, A772–A799, 2021. RG, ArXiv, Code

[16] Shuai Li, Shenglong Zhou, and Ziyan Luo, Sparse quadratically constrained quadratic programming via semismooth Newton method, Mathematical Programming Computation, 2026. RG, ArXiv, Code

[15] Jun Fan, Jie Sun, Ailin Yan, and Shenglong Zhou, An oracle gradient regularized Newton method for quadratic measurements regression, Applied and Computational Harmonic Analysis, 78, 101775, 2025. RG, ArXiv

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[14] Shenglong Zhou, Lili Pan, and Naihua Xiu, Newton method for L0-regularized optimization, Numerical Algorithms, 88, 1541–1570, 2021. RG, ArXiv, Code

[13] Shenglong Zhou, Xianchao Xiu, Yingnan Wang, and Dingtao Peng, Revisiting Lq (0<=q<1) norm regularized optimization, 2023. RG, ArXiv, Code

[12] Jun Sun, Lingchen Kong, and Shenglong Zhou, Gradient projection Newton algorithm for sparse collaborative learning, Journal of Computational and Applied Mathematics, 422, 1-20, 2022. RG, ArXiv

[11] Rui Wang, Naihua Xiu, and Shenglong Zhou, An extended Newton-type algorithm for L2-regularized sparse logistic regression and its efficiency for classifying large-scale datasets, Journal of Computational and Applied Mathematics, 397, 1-17, 2021. RG, ArXiv, Code

[10] Xinrong Li, Naihua Xiu, and Shenglong Zhou, Matrix optimization over low-rank spectral sets: stationary points, local and global minimizers, Journal of Optimization Theory and Applications, 184, 895–930, 2019. RG

[9] Lili Pan, Shenglong Zhou, Naihua Xiu, and Huoduo Qi, A convergent iterative hard thresholding for sparsity and nonnegativity constrained optimization, Pacific Journal of Optimization, 13, 325-353, 2017. RG, Code

[8] Lianjun Zhang, Lingchen Kong, and Shenglong Zhou, A smoothing iterative method for quantile regression with nonconvex lp Penalty, Journal of Industrial and Management Optimization, 13, 93-112, 2017.

[7] Yanqing Liu, Guokai Liu, Xianchao Xiu, and Shenglong Zhou, The L1-penalized quantile regression for traditional Chinese medicine syndrome manifestation, Pacific Journal of Optimization, 13, 279-300, 2017.

[6] Shenglong Zhou, Naihua Xiu, Yingnan Wang, Lingchen Kong, and Huoduo Qi, A Null-space-based weighted l1 minimization approach to compressed sensing, Information and Inference: A Journal of the IMA , 5, 76-102, 2016. RG, Code

[5] Lili Pan, Naihua Xiu, and Shenglong Zhou, On Solutions of Sparsity Constrained Optimization, Journal of the Operations Research Society of China, 3, 421-439, 2015.

[4] Shenglong Zhou, Naihua Xiu, Ziyan Luo, and Lingchen Kong, Sparse and low-rank covariance matrix estimation, Journal of the Operations Research Society of China, 3, 231-250, 2015. Code

[3] Meijuan Shang, Shenglong Zhou, and Naihua Xiu, Extragradient thresholding methods For sparse solutions of co-coercive NCPs, Journal of Inequalities and Applications, 34, 2015. Code

[2] Meijuan Shang, Chao Zhang, Dingtao Peng, and Shenglong Zhou, A half thresholding projection algorithm for sparse solutions of LCPs, Optimization Letters, 9, 1231-1245, 2015. Code

[1] Shenglong Zhou, Lingchen Kong, and Naihua Xiu, New bounds for RIC in compressed sensing, Journal of the Operations Research Society of China, 1, 227-237, 2013.

[13] Shenglong Zhou, Ouya Wang, Ziyan Luo, Yongxu Zhu, and Geoffrey Ye Li, Preconditioned inexact stochastic ADMM for deep models, Nature Machine Intelligence, 8, 234-245, 2026. RG, ArXiv, Code

[12] Shenglong Zhou and Geoffrey Ye Li, Federated learning via inexact ADMM, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 9699-9708, 2023. RG, ArXiv, Code

[11] Shenglong Zhou and Geoffrey Ye Li, FedGiA: An efficient hybrid algorithm for federated learning, IEEE Transactions on Signal Processing , 71, 1493-1508, 2023. RG, ArXiv, Code

[10] Kaidi Xu, Shenglong Zhou, and Geoffrey Ye Li, Neural collapse based deep supervised federated learning for signal detection in OFDM systems, IEEE Transactions on Signal Processing, 74, 1778-1788, 2026. RG, ArXiv

[9] Shan Sha, Shenglong Zhou, Xin Wang, Lingchen Kong, Geoffrey Ye Li, Decentralized federated learning by partial message exchange, 2026. RG, ArXiv

[8] Shan Sha, Shenglong Zhou, Lingchen Kong, and Geoffrey Ye Li, Sparse decentralized federated learning, IEEE Transactions on Signal Processing, 73, 3406-3420, 2025. RG, ArXiv

[7] Ouya Wang, Shenglong Zhou, and Geoffrey Ye Li, Frameworks on few-shot learning with applications in wireless communication, IEEE Transactions on Signal Processing, 73, 3857-3871, 2025. RG, ArXiv

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[6] Kaidi Xu, Shenglong Zhou, and Geoffrey Ye Li, Federated reinforcement learning for resource allocation in V2X networks, IEEE Journal of Selected Topics in Signal Processing, 18, 1210-1221, 2024. RG, ArXiv

[5] Ouya Wang, Hengtao He, Shenglong Zhou, Zhi Ding, Shi Jin, Khaled Letaief, and Geoffrey Ye Li, Fast adaptation for deep learning-based wireless communications, IEEE Communications Magazine, 63, 158-164, 2025.

[4] Ouya Wang, Shenglong Zhou, and Geoffrey Ye Li, BADM: Batch ADMM for deep learning, 2024. RG, ArXiv

[3] Kaidi Xu, Shenglong Zhou, and Geoffrey Ye Li, Rescale-invariant federated reinforcement learning for resource allocation in V2X networks, IEEE Communications Letters, 1-5, 2024. RG, ArXiv

[2] Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang, Shenglong Zhou, and Geoffrey Ye Li, Accretionary learning with deep neural networks, IEEE Transactions on Cognitive Communications and Networking , 2023. RG, ArXiv

[1] Shenglong Zhou and Geoffrey Ye Li, Exact penalty method for federated learning, 2022. RG, ArXiv, Code

[3] Shenglong Zhou, Naihua Xiu, and Huoduo Qi, Robust Euclidean embedding via EDM optimization, Mathematical Programming Computation, 12, 337–387, 2019. Code

[2] Shenglong Zhou, Naihua Xiu, and Huoduo Qi, A fast matrix majorization-projection method for penalized stress minimization with box constraints, IEEE Transactions on Signal Processing, 66, 4331-4346, 2018. Code

[1] Shenglong Zhou, Majorization-projection methods for multidimensional scaling via Euclidean distance matrix optimization, PhD Thesis, University of Southampton, 2018.

[4] Shenglong Zhou, Alain Zemkoho, and Andrey Tin, BOLIB 2019: Bilevel Optimization LIBrary of Test Problems Version 2, 2019. Bilevel optimization: advances and next challenges, BiOpt, RG, ArXiv, Code

[3] Joydeep Dutta, Lafhim Lahoussine, Alain B. Zemkoho, and Shenglong Zhou, Nonconvex quasi-variational inequalities: stability analysis and application to numerical optimization, Journal of Optimization Theory and Applications, 204(16), 625-674, 2025. RG, ArXiv,

[2] Alain Zemkoho and Shenglong Zhou, Theoretical and numerical comparison of the KKT and value function reformulations in bilevel optimization, Computational Optimization and Application, 78, 625-674, 2021. RG, ArXiv

[1] Andreas Fischer, Alain Zemkoho, and Shenglong Zhou, Semismooth Newton-type method for bilevel optimization: Global convergence and extensive numerical experiments, Optimization Methods and Software, 1-35, 2021. RG, ArXiv

[4] Shenglong Zhou, Kaidi Xu, and Geoffrey Ye Li, Communication-efficient decentralized federated learning via one-bit compressive sensing, 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring) , 2024. RG, ArXiv

[3] Kaidi Xu, Shenglong Zhou, and Geoffrey Ye Li, Federated reinforcement learning for resource allocation in V2X networks, 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 1-6, 2024. RG

[2] Ouya Wang, Shenglong Zhou, and Geoffrey Ye Li, Effective adaptation into new environment with few shots: Applications to OFDM receiver design, 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023. RG (Top 5% Outstanding Paper)

[1] Ouya Wang, Shenglong Zhou, and Geoffrey Ye Li, Few-shot learning for new environment adaptation, 2023 IEEE Global Communications Conference, 2023. RG