Search Results for author: Yankai Cao

Found 6 papers, 3 papers with code

High-Order Tensor Recovery with A Tensor $U_1$ Norm

1 code implementation23 Nov 2023 Jingjing Zheng, Wenzhe Wang, Xiaoqin Zhang, Yankai Cao, Xianta Jiang

An optimization algorithm is proposed to solve the resulting tensor completion model iteratively by combining the proximal algorithm with the Alternating Direction Method of Multipliers.

Tensor Decomposition

A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets

no code implementations12 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.

A Global Optimization Algorithm for K-Center Clustering of One Billion Samples

no code implementations30 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.

Clustering

A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization

no code implementations6 Jul 2022 Ahmed Al-Shafei, Hamidreza Zareipour, Yankai Cao

This article recounts the dawn of parallel computation and its appearance in power system studies, reviewing the most recent literature and research on exploiting the available computational resources and technologies today.

Cloud Computing

Deep Learning-based Predictive Control of Battery Management for Frequency Regulation

1 code implementation4 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.

Management Model Predictive Control +1

Graph-Based Modeling and Simulation of Complex Systems

4 code implementations12 Dec 2018 Jordan Jalving, Yankai Cao, Victor M. Zavala

We present graph-based modeling abstractions to represent cyber-physical dependencies arising in complex systems.

Optimization and Control

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