no code implementations • 19 Apr 2024 • Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang, Zhihao Shi, Fangzhou Zhu, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu
Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well.
no code implementations • 11 Jan 2024 • Xijun Li, Fangzhou Zhu, Hui-Ling Zhen, Weilin Luo, Meng Lu, Yimin Huang, Zhenan Fan, Zirui Zhou, Yufei Kuang, Zhihai Wang, Zijie Geng, Yang Li, Haoyang Liu, Zhiwu An, Muming Yang, Jianshu Li, Jie Wang, Junchi Yan, Defeng Sun, Tao Zhong, Yong Zhang, Jia Zeng, Mingxuan Yuan, Jianye Hao, Jun Yao, Kun Mao
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
no code implementations • 18 Oct 2023 • Yufei Kuang, Xijun Li, Jie Wang, Fangzhou Zhu, Meng Lu, Zhihai Wang, Jia Zeng, Houqiang Li, Yongdong Zhang, Feng Wu
Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently.
no code implementations • 23 Jul 2023 • Rui Meng, Fangzhou Zhu, Xiaodong Xu, Liang Jin, Bizhu Wang, Bingxuan Xu, Han Meng, Ping Zhang
Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals.
no code implementations • 17 Jan 2022 • Xijun Li, Qingyu Qu, Fangzhou Zhu, Jia Zeng, Mingxuan Yuan, Kun Mao, Jie Wang
In the past decades, a serial of traditional operation research algorithms have been proposed to obtain the optimum of a given LP in a fewer solving time.
2 code implementations • 11 Jul 2016 • Yuting Wang, Gong-Bo Zhao, Chia-Hsun Chuang, Ashley J. Ross, Will J. Percival, Héctor Gil-Marín, Antonio J. Cuesta, Francisco-Shu Kitaura, Sergio Rodriguez-Torres, Joel R. Brownstein, Daniel J. Eisenstein, Shirley Ho, Jean-Paul Kneib, Matt Olmstead, Francisco Prada, Graziano Rossi, Ariel G. Sánchez, Salvador Salazar-Albornoz, Daniel Thomas, Jeremy Tinker, Rita Tojeiro, Mariana Vargas-Magaña, Fangzhou Zhu
Splitting the sample into multiple overlapping redshift slices to extract the redshift information of galaxy clustering, we obtain a measurement of $D_A(z)/r_d$ and $H(z)r_d$ at nine effective redshifts with the full covariance matrix calibrated using MultiDark-Patchy mock catalogues.
Cosmology and Nongalactic Astrophysics