Zobrazeno 1 - 9
of 9
pro vyhledávání: '"KANGKANG DENG"'
Autor:
Kangkang, Deng, Zheng, Peng
We consider a nonsmooth optimization problem on Riemannian manifold, whose objective function is the sum of a differentiable component and a nonsmooth convex function. We propose a manifold inexact augmented Lagrangian method (MIALM) for the consider
Externí odkaz:
http://arxiv.org/abs/1911.09900
Autor:
YIFEI WANG1 wangyf18@stanford.edu, KANGKANG DENG2 dengkangkang@pku.edu.cn, HAOYANG LIU2 liuhaoyang@pku.edu.cn, ZAIWEN WEN3 wenzw@pku.edu.cn
Publikováno v:
SIAM Journal on Optimization. 2023, Vol. 33 Issue 3, p1361-1390. 30p.
Autor:
Kangkang Deng, Zheng Peng
Publikováno v:
IMA Journal of Numerical Analysis.
We develop a manifold inexact augmented Lagrangian framework to solve a family of nonsmooth optimization problem on Riemannian submanifold embedding in Euclidean space, whose objective function is the sum of a smooth function (but possibly nonconvex)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a3b08a8e0f8d54cb9239a0b9df1c920
https://doi.org/10.1093/imanum/drac018
https://doi.org/10.1093/imanum/drac018
Publikováno v:
SIAM Journal on Imaging Sciences. 13:1446-1466
The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a564692339bd425e3602dea67a90631
https://doi.org/10.1137/19m1253873
https://doi.org/10.1137/19m1253873
Publikováno v:
Journal of Global Optimization.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35c9d1044db3a2d6f12d8d752388ba7f
https://doi.org/10.1007/s10898-022-01144-0
https://doi.org/10.1007/s10898-022-01144-0
Publikováno v:
Bulletin of the Iranian Mathematical Society. 46:865-892
In this paper, we propose a new customized proximal point algorithm for linearly constrained convex optimization problem, and further extend the proposed method to separable convex optimization problem. Unlike the existing customized proximal point a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2071d7695b67324446ad94a63b08778
https://doi.org/10.1007/s41980-019-00298-0
https://doi.org/10.1007/s41980-019-00298-0
Publikováno v:
Journal of Industrial & Management Optimization. 15:1881-1896
The sparse probabilistic Boolean network (SPBN) model has been applied in various fields of industrial engineering and management. The goal of this model is to find a sparse probability distribution based on a given transition-probability matrix and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7bf215a05088c16a7c3f43e6efda49e6
https://doi.org/10.3934/jimo.2018127
https://doi.org/10.3934/jimo.2018127
Autor:
Bafan Huang, Kangkang Deng
Publikováno v:
2019 6th International Conference on Information Science and Control Engineering (ICISCE).
Sparse representation based classification (SRC) has gained great success in image recognition. In this paper, we propose a generalized CRC method, essentially a SRC using Euler sparse representation, for image classification. To be specific, our CRC
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31d50f452b562321df46f104581a813f
https://doi.org/10.1109/icisce48695.2019.00115
https://doi.org/10.1109/icisce48695.2019.00115
Publikováno v:
SIAM Journal on Imaging Sciences; 2020, Vol. 13 Issue 3, p1446-1466, 21p