Zobrazeno 1 - 10
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pro vyhledávání: '"LIANG Ling"'
Inexact proximal augmented Lagrangian methods (pALMs) are particularly appealing for tackling convex constrained optimization problems because of their elegant convergence properties and strong practical performance. To solve the associated pALM subp
Externí odkaz:
http://arxiv.org/abs/2411.13267
Autor:
Ma, Qichao, Zhu, Rui-Jie, Liu, Peiye, Yan, Renye, Zhang, Fahong, Liang, Ling, Li, Meng, Yu, Zhaofei, Wang, Zongwei, Cai, Yimao, Huang, Tiejun
Large Language Models (LLMs) have become pervasive due to their knowledge absorption and text-generation capabilities. Concurrently, the copyright issue for pretraining datasets has been a pressing concern, particularly when generation includes speci
Externí odkaz:
http://arxiv.org/abs/2410.04454
Autor:
Liang, Ling, Yang, Haizhao
Computing the exact optimal experimental design has been a longstanding challenge in various scientific fields. This problem, when formulated using a specific information function, becomes a mixed-integer nonlinear programming (MINLP) problem, which
Externí odkaz:
http://arxiv.org/abs/2409.18392
Learning to Optimize (L2O) approaches, including algorithm unrolling, plug-and-play methods, and hyperparameter learning, have garnered significant attention and have been successfully applied to the Alternating Direction Method of Multipliers (ADMM)
Externí odkaz:
http://arxiv.org/abs/2409.17320
The imbalance of exploration and exploitation has long been a significant challenge in reinforcement learning. In policy optimization, excessive reliance on exploration reduces learning efficiency, while over-dependence on exploitation might trap age
Externí odkaz:
http://arxiv.org/abs/2408.09974
Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands. However, their deployment on edge devices still faces significant challenges due to hi
Externí odkaz:
http://arxiv.org/abs/2408.10284
Autor:
Han, Husheng, Zheng, Xinyao, Wen, Yuanbo, Hao, Yifan, Feng, Erhu, Liang, Ling, Mu, Jianan, Li, Xiaqing, Ma, Tianyun, Jin, Pengwei, Song, Xinkai, Du, Zidong, Guo, Qi, Hu, Xing
Heterogeneous collaborative computing with NPU and CPU has received widespread attention due to its substantial performance benefits. To ensure data confidentiality and integrity during computing, Trusted Execution Environments (TEE) is considered a
Externí odkaz:
http://arxiv.org/abs/2407.08903
A vertex exchange method is proposed for solving the strongly convex quadratic program subject to the generalized simplex constraint. We conduct rigorous convergence analysis for the proposed algorithm and demonstrate its essential roles in solving s
Externí odkaz:
http://arxiv.org/abs/2407.03294
Solving symmetric positive semidefinite linear systems is an essential task in many scientific computing problems. While Jacobi-type methods, including the classical Jacobi method and the weighted Jacobi method, exhibit simplicity in their forms and
Externí odkaz:
http://arxiv.org/abs/2407.03272
We propose semidefinite trajectory optimization (STROM), a framework that computes fast and certifiably optimal solutions for nonconvex trajectory optimization problems defined by polynomial objectives and constraints. STROM employs sparse second-ord
Externí odkaz:
http://arxiv.org/abs/2406.05846