Zobrazeno 1 - 10
of 1 094
pro vyhledávání: '"Ma Yuxin"'
Autor:
Carson, Erin, Ma, Yuxin
Communication, i.e., data movement, is a critical bottleneck for the performance of classical Krylov subspace method solvers on modern computer architectures. Variants of these methods which avoid communication have been introduced, which, while equi
Externí odkaz:
http://arxiv.org/abs/2409.03079
Interest in communication-avoiding orthogonalization schemes for high-performance computing has been growing recently. This manuscript addresses open questions about the numerical stability of various block classical Gram-Schmidt variants that have b
Externí odkaz:
http://arxiv.org/abs/2408.10109
Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful tools for solving complex optimization problems characterized by multiple, often conflicting, objectives. While advancements have been made in computational efficiency as well a
Externí odkaz:
http://arxiv.org/abs/2408.04539
Recent development on mixed precision techniques has largely enhanced the performance of various linear algebra solvers, one of which being the least squares problem $\min_{x}\lVert b-Ax\rVert_{2}$. By transforming the least squares problem into an a
Externí odkaz:
http://arxiv.org/abs/2406.16499
Block classical Gram-Schmidt (BCGS) is commonly used for orthogonalizing a set of vectors $X$ in distributed computing environments due to its favorable communication properties relative to other orthogonalization approaches, such as modified Gram-Sc
Externí odkaz:
http://arxiv.org/abs/2405.01298
Autor:
Feng, Zezheng, Jiang, Yifan, Wang, Hongjun, Fan, Zipei, Ma, Yuxin, Yang, Shuang-Hua, Qu, Huamin, Song, Xuan
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decisions in traffic control. However, most state-of-the-art DL models are con
Externí odkaz:
http://arxiv.org/abs/2403.04812
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather. Accurate prediction of spatiotemporal series remains challenging due to the complex spatiotemporal heterogeneity. In particular, cu
Externí odkaz:
http://arxiv.org/abs/2312.00516
Autor:
Lei, Fan, Ma, Yuxin, Fotheringham, Stewart, Mack, Elizabeth, Li, Ziqi, Sachdeva, Mehak, Bardin, Sarah, Maciejewski, Ross
Geographic regression models of various descriptions are often applied to identify patterns and anomalies in the determinants of spatially distributed observations. These types of analyses focus on answering why questions about underlying spatial phe
Externí odkaz:
http://arxiv.org/abs/2308.13588
Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems. In real-world applications, analysts often employ several algorithms concurrently and compare their
Externí odkaz:
http://arxiv.org/abs/2308.05640