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pro vyhledávání: '"Proximal-gradient methods"'
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Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:5933-5946
Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33e1fb33982d7ce0701522a2935d3175
https://doi.org/10.1109/tpami.2021.3087328
https://doi.org/10.1109/tpami.2021.3087328
Publikováno v:
IEEE Transactions on Cybernetics. 52:11093-11106
Traditional target detection methods assume that the background spectrum is subject to the Gaussian distribution, which may only perform well under certain conditions. In addition, traditional target detection methods suffer from the problem of the u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11b80dd14e8e1c7329af0e855355d4fd
https://doi.org/10.1109/tcyb.2021.3070909
https://doi.org/10.1109/tcyb.2021.3070909
Autor:
Hao Luo
Publikováno v:
Optimization. 72:1139-1170
This work introduces a second-order differential inclusion for unconstrained convex optimization. In continuous level, solution existence in proper sense is obtained and exponential decay of a novel Lyapunov function along with the solution trajector
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f474ef9bf03598797e26b1d807badd0e
https://doi.org/10.1080/02331934.2021.2002327
https://doi.org/10.1080/02331934.2021.2002327
Autor:
Shummin Nakayama, Jun-ya Gotoh
Publikováno v:
Optimization Letters. 15(8):2831-2860
This paper conducts a comparative study of proximal gradient methods (PGMs) and proximal DC algorithms (PDCAs) for sparse regression problems which can be cast as Difference-of-two-Convex-functions (DC) optimization problems. It has been shown that f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16a7a416ea051dad2f0ce55f0a094b37
http://id.nii.ac.jp/1438/00010242/
http://id.nii.ac.jp/1438/00010242/
Publikováno v:
International Journal of Information Technology & Decision Making. 21:169-194
Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the ou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22cb2c03542ab05d1cf18e2d997b40ee
https://doi.org/10.1142/s0219622021500553
https://doi.org/10.1142/s0219622021500553
Publikováno v:
Journal of Optimization Theory and Applications. 193:324-353
This paper studies the minimization of a broad class of nonsmooth nonconvex objective functions subject to nonlinear functional equality constraints, where the gradients of the differentiable parts in the objective and the constraints are only locall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55af66571eaccbdfc90d4e15e7a9779b
https://doi.org/10.1007/s10957-021-01929-5
https://doi.org/10.1007/s10957-021-01929-5
Publikováno v:
Acta Mathematica Scientia. 42:403-419
In this paper, we consider 3D tomographic reconstruction for axially symmetric objects from a single radiograph formed by cone-beam X-rays. All contemporary density reconstruction methods in high-energy X-ray radiography are based on the assumption t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c28ccfbab8ea0e4e587c2472d1ff70dd
https://doi.org/10.1007/s10473-022-0122-z
https://doi.org/10.1007/s10473-022-0122-z