no code implementations • 12 Nov 2023 • Jiayang Ren, Valentín Osuna-Enciso, Morimasa Okamoto, Qiangqiang Mao, Chaojie Ji, Liang Cao, Kaixun Hua, Yankai Cao
Decision trees are essential yet NP-complete to train, prompting the widespread use of heuristic methods such as CART, which suffers from sub-optimal performance due to its greedy nature.
no code implementations • 30 Dec 2022 • Jiayang Ren, Ningning You, Kaixun Hua, Chaojie Ji, Yankai Cao
This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance.
1 code implementation • 4 Jan 2022 • Yun Li, Yixiu Wang, Yifu Chen, Kaixun Hua, Jiayang Ren, Ghazaleh Mozafari, Qiugang Lu, Yankai Cao
The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation.