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of 49
pro vyhledávání: '"Lin, Zhenwei"'
In this paper, we address a long-standing challenge: how to achieve both efficiency and scalability in solving semidefinite programming problems. We propose breakthrough acceleration techniques for a wide range of low-rank factorization-based first-o
Externí odkaz:
http://arxiv.org/abs/2407.15049
Robust Markov Decision Processes (RMDPs) have recently been recognized as a valuable and promising approach to discovering a policy with creditable performance, particularly in the presence of a dynamic environment and estimation errors in the transi
Externí odkaz:
http://arxiv.org/abs/2406.00274
Autor:
Han, Qiushi, Li, Chenxi, Lin, Zhenwei, Chen, Caihua, Deng, Qi, Ge, Dongdong, Liu, Huikang, Ye, Yinyu
We introduce a new first-order method for solving general semidefinite programming problems, based on the alternating direction method of multipliers (ADMM) and a matrix-splitting technique. Our algorithm has an advantage over the Burer-Monteiro appr
Externí odkaz:
http://arxiv.org/abs/2403.09133
We consider decentralized gradient-free optimization of minimizing Lipschitz continuous functions that satisfy neither smoothness nor convexity assumption. We propose two novel gradient-free algorithms, the Decentralized Gradient-Free Method (DGFM) a
Externí odkaz:
http://arxiv.org/abs/2310.11973
Autor:
Lin, Zhenwei, Deng, Qi
As the complexity of learning tasks surges, modern machine learning encounters a new constrained learning paradigm characterized by more intricate and data-driven function constraints. Prominent applications include Neyman-Pearson classification (NPC
Externí odkaz:
http://arxiv.org/abs/2308.10767
Autor:
Lin, Zhenwei, Deng, Qi
In this paper, we introduce faster first-order primal-dual algorithms for minimizing a convex function subject to strongly convex function constraints. Before our work, the best complexity bound was $\mathcal{O}(1/{\varepsilon})$, and it remains uncl
Externí odkaz:
http://arxiv.org/abs/2212.11143
Autor:
Huang, Tianyu, Lai, Meng, Lin, Zhenwei, Luo, Ruiqi, Xiang, Xuezheng, Xu, Haozhe, Pan, Ning, Zuo, Zhaojiang
Publikováno v:
In Environmental Research 15 January 2024 241
Publikováno v:
IET Energy Systems Integration; Dec2023, Vol. 5 Issue 4, p477-490, 14p
Akademický článek
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Publikováno v:
IEEE Sensors Journal. 21:5185-5194
An adaptive beamformer is effective at suppressing interference and noise. However, when the desired signal component is included in the covariance matrix, the beamformer performance becomes seriously degraded. Moreover, while the linear array has be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc8887491b52f75f39660292ccf1850f
https://doi.org/10.1109/jsen.2020.3030043
https://doi.org/10.1109/jsen.2020.3030043