scholarly journals A Low-Rank Tensor Method for PDE-Constrained Optimization with Isogeometric Analysis

2020 ◽  
Vol 42 (1) ◽  
pp. A140-A161 ◽  
Author(s):  
Alexandra Bünger ◽  
Sergey Dolgov ◽  
Martin Stoll
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jinzhi Liao ◽  
Jiuyang Tang ◽  
Xiang Zhao ◽  
Haichuan Shang

POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks. In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind. In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor. Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm. In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model. Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.


2017 ◽  
Vol 316 ◽  
pp. 1062-1085 ◽  
Author(s):  
Angelos Mantzaflaris ◽  
Bert Jüttler ◽  
Boris N. Khoromskij ◽  
Ulrich Langer

Author(s):  
Angelos Mantzaflaris ◽  
Bert Jüttler ◽  
B. N. Khoromskij ◽  
Ulrich Langer

Author(s):  
Yongyong Chen ◽  
Xiaolin Xiao ◽  
Chong Peng ◽  
Guangming Lu ◽  
Yicong Zhou

Author(s):  
Alexey I. Boyko ◽  
Mikhail P. Matrosov ◽  
Ivan V. Oseledets ◽  
Dzmitry Tsetserukou ◽  
Gonzalo Ferrer

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