著者
鈴木 大慈
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.28, no.4, pp.28-33, 2018-12-21 (Released:2019-03-31)
参考文献数
20
被引用文献数
2
著者
行木 孝夫
出版者
日本応用数理学会
雑誌
応用数理 (ISSN:09172270)
巻号頁・発行日
vol.13, no.2, pp.125-136, 2003-06

ライフゲームに代表されるセルオートマトンは簡明な定義からなる系であ りながら多様な挙動を示すものとして広く研究されてきた。 本稿では エルゴード理論、力学系のごく簡単な導入を行い、 エントロピーや変分原理など関連する事項を整理するとともに 力学系としてのセルオートマトンの特徴を明らかにする。
著者
伊理 正夫
出版者
一般社団法人日本応用数理学会
雑誌
応用数理 (ISSN:09172270)
巻号頁・発行日
vol.3, no.1, pp.58-66, 1993-03-15
著者
蔵本 由紀
出版者
一般社団法人日本応用数理学会
雑誌
応用数理 (ISSN:09172270)
巻号頁・発行日
vol.17, no.2, pp.175-177, 2007-06-26
著者
矢田部 浩平
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.33, no.1, pp.14-25, 2023-03-24 (Released:2023-06-30)
参考文献数
30

Optimization tools have been extensively employed in signal processing. Recent advances in optimization algorithms based on proximity operators have broadened the application range of optimization-based signal processing. Moreover, deep learning has rapidly developed a new signal processing scheme that can perform notably better than conventional ones. Consequently, they have been combined in some studies. In this paper, we briefly review studies combining proximal splitting algorithms and deep learning.
著者
鈴木 大慈
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.28, no.2, pp.28-33, 2018-06-26 (Released:2018-09-30)
参考文献数
16
被引用文献数
5
著者
長井 英生
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.13, no.4, pp.318-333, 2003-12-25 (Released:2017-04-08)
参考文献数
42
被引用文献数
2

We give an overview of the studies of stochastic control and filtering theory, tracing the historical situation from Kalman-Bucy filtering, LQG stochastic control theory and their mathematical generalization to nonlinear systems and nonlinear filtering to H^∞ control and risk-sensitive stochastic control. Then we explain how we could formulate portfolio optimization problems for Merton's ICAPM, which are typical ones on mathematical finance, as risk-sensitive stochastic control problems based on understanding the situation, and analyze them by employing the methods established through such studies. Dynamic programming approach to stochastic control and the methods of measure change in nonlinear filtering apply to obtain explicit representation of optimal strategies for the portfolio optimization problems. More other aspects could be seen.
著者
池口 徹 合原 一幸
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.7, no.4, pp.260-270, 1997-12-15 (Released:2017-04-08)
参考文献数
25
被引用文献数
1

This paper reviews such embedding theorems of dynamical systems as Takens' embedding theory and the extended theory by Sauer et al. and practical methods to reconstruct possible attractors only from observed time series data. These are bases for time series analysis from the view point of nonlinear dynamical systems theory. The reconstruction method by filtered delay coordinates is also discussed.
著者
池 祐一
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.32, no.3, pp.139-148, 2022-09-22 (Released:2022-12-26)
参考文献数
14

The recent applications of topological data analysis in machine learning are reviewed in this paper. Simplicial and persistent homology were briefly explained and then two such applications were described. The first application is a topological study of the activation of neural networks, and the second application is a convergence result for the stochastic subgradient method for topological loss functions.
著者
広田 良吾
出版者
一般社団法人日本応用数理学会
雑誌
応用数理 (ISSN:09172270)
巻号頁・発行日
vol.14, no.1, pp.62-66, 2004-03-25
被引用文献数
1
著者
保國 惠一
出版者
一般社団法人 日本応用数理学会
雑誌
応用数理 (ISSN:24321982)
巻号頁・発行日
vol.28, no.2, pp.11-18, 2018 (Released:2018-09-30)
参考文献数
31

Linear systems involving singularity arise in a wide range of applications throughout computational science and engineering. This article aims at presenting and discussing iterative methods for solving linear systems with singularity, with an emphasis on stationary (matrix splitting) and Krylov subspace iterative methods and preconditioning techniques. For singular matrices, conventional preconditioners based on incomplete matrix factorizations may break down, whereas particular stationary iterative methods combined with Krylov subspace methods may avoid breakdown. Although classical stationary iterative methods have been regarded as slow to converge, recent stationary iterative methods have convergence speed competitive with Krylov subspace methods, and may dramatically improve the convergence of Krylov subspace methods when applied as preconditioners. We present recent results on their convergence theories in general and for particular problems such as saddle point systems and least squares problems.