Zobrazeno 1 - 7
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pro vyhledávání: '"Shimmura, Ryosuke"'
Autor:
Shimmura, Ryosuke, Suzuki, Joe
In this paper, we propose new methods to efficiently solve convex optimization problems encountered in sparse estimation, which include a new quasi-Newton method that avoids computing the Hessian matrix and improves efficiency, and we prove its fast
Externí odkaz:
http://arxiv.org/abs/2211.16804
We consider learning an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning for join
Externí odkaz:
http://arxiv.org/abs/2107.07799
Autor:
Shimmura, Ryosuke, Suzuki, Joe
In sparse estimation, such as fused lasso and convex clustering, we apply either the proximal gradient method or the alternating direction method of multipliers (ADMM) to solve the problem. It takes time to include matrix division in the former case,
Externí odkaz:
http://arxiv.org/abs/2104.10911
Autor:
Shimmura, Ryosuke1 (AUTHOR) prof.joe.suzuki@gmail.com, Suzuki, Joe1 (AUTHOR)
Publikováno v:
Entropy. Jan2024, Vol. 26 Issue 1, p44. 13p.
Autor:
Shimmura, Ryosuke, Suzuki, Joe
In sparse estimation, in which the sum of the loss function and the regularization term is minimized, methods such as the proximal gradient method and the proximal Newton method are applied. The former is slow to converge to a solution, while the lat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9b250752206ac53807650f73fdd7edb
Autor:
Shimmura, Ryosuke, Suzuki, Joe
Publikováno v:
Japanese Journal of Statistics & Data Science; Dec2022, Vol. 5 Issue 2, p725-745, 21p
Autor:
Shimmura R; Graduate School of Engineer Science, Osaka University, Toyonaka 560-0043, Japan., Suzuki J; Graduate School of Engineer Science, Osaka University, Toyonaka 560-0043, Japan.
Publikováno v:
Entropy (Basel, Switzerland) [Entropy (Basel)] 2023 Dec 31; Vol. 26 (1). Date of Electronic Publication: 2023 Dec 31.