[5] Shenglong Zhou, Lili Pan, Naihua Xiu, and Geoffrey Ye Li, 0/1 constrained optimization solving sample average approximation for chance constrained programming, 2022. RG, ArXiv

[4] 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

[3] 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

[2] 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

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

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

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

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

[15] 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

[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, 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

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[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.

[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.

[9] Ouya Wang, Shenglong Zhou, and Geoffrey Ye Li, New environment adaptation with few shots for OFDM receiver and mmWave beamforming, 2023. RG, ArXiv

[8] Kaidi Xu, Shenglong Zhou, and Geoffrey Ye Li, Federated reinforcement learning for resource allocation in V2X networks, 2023. RG, ArXiv

[7] Shenglong Zhou, Kaidi Xu, and Geoffrey Ye Li, Communication-efficient decentralized federated learning via one-bit compressive sensing, 2023. RG, ArXiv

[6] 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

[5] 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

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

[3] Hui Zhang, Shenglong Zhou, Naihua Xiu, and Geoffrey Ye Li, 0/1 Deep neural networks via block coordinate descent, 2022. RG, ArXiv

[2] Shenglong Zhou and Geoffrey Ye Li, Communication-efficient ADMM-based federated learning, 2021. RG, ArXiv, Code

[1] 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

[4] Joydeep Dutta, Lafhim Lahoussine, Alain B. Zemkoho, and Shenglong Zhou, Nonconvex quasi-variational inequalities: stability analysis and application to numerical optimization, 2022. RG, ArXiv,

[3] 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

[2] 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

[1] 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