Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Gradient descent methods"'
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2023, Iss 1, Pp 1-22 (2023)
Abstract Wireless localization technology has been widely used in indoor and outdoor fields. Channel estimation based on channel state information is a hot research topic in recent years. However, due to the interference of acquisition bandwidth, noi
Externí odkaz:
https://doaj.org/article/edd42b1318484127933b257631756c80
Autor:
Predrag S. Stanimirović, Bilall I. Shaini, Jamilu Sabi’u, Abdullah Shah, Milena J. Petrović, Branislav Ivanov, Xinwei Cao, Alena Stupina, Shuai Li
Publikováno v:
Algorithms, Vol 16, Iss 2, p 64 (2023)
This research proposes and investigates some improvements in gradient descent iterations that can be applied for solving system of nonlinear equations (SNE). In the available literature, such methods are termed improved gradient descent methods. We u
Externí odkaz:
https://doaj.org/article/acd97058c9f94823aa26149d776106ee
Autor:
Predrag S. Stanimirović, Branislav Ivanov, Dragiša Stanujkić, Vasilios N. Katsikis, Spyridon D. Mourtas, Lev A. Kazakovtsev, Seyyed Ahmad Edalatpanah
Publikováno v:
Symmetry, Vol 15, Iss 1, p 250 (2023)
The influence of neutrosophy on many fields of science and technology, as well as its numerous applications, are evident. Our motivation is to apply neutrosophy for the first time in order to improve methods for solving unconstrained optimization. Pa
Externí odkaz:
https://doaj.org/article/637b477575564479b2c294c1c96c7af9
Autor:
Vladimir Rakočević, Milena J. Petrović
Publikováno v:
Mathematics, Vol 10, Iss 23, p 4411 (2022)
In this paper, we follow a chronological development of gradient descent methods and its accelerated variants later on. We specifically emphasise some contemporary approaches within this research field. Accordingly, a constructive overview over the c
Externí odkaz:
https://doaj.org/article/c4877bd10a9546b7a6c281c4e1b39c59
Publikováno v:
The University Thought: Publication in Natural Sciences, Vol 9, Iss 1, Pp 57-61 (2019)
We analyze a performance profile of several accelerated and hybrid accelerated methods. All comparative methods are at least linearly convergent and have satisfied numerical characteristics regarding tested metrics: number of iterations, CPU time and
Externí odkaz:
https://doaj.org/article/f0463b40112a4c3d9ad1475195f83d68
Publikováno v:
Applied Sciences, Vol 12, Iss 18, p 9389 (2022)
Adaptive gradient descent methods such as Adam, RMSprop, and AdaGrad achieve great success in training deep learning models. These methods adaptively change the learning rates, resulting in a faster convergence speed. Recent studies have shown their
Externí odkaz:
https://doaj.org/article/50d05d070bb9466984094521054bb953
Publikováno v:
Mathematics, Vol 10, Iss 2, p 259 (2022)
We propose an improved variant of the accelerated gradient optimization models for solving unconstrained minimization problems. Merging the positive features of either double direction, as well as double step size accelerated gradient models, we defi
Externí odkaz:
https://doaj.org/article/b7924f9c746348c19f34f239bb5d71f4
Publikováno v:
The University Thought: Publication in Natural Sciences, Vol 7, Iss 1, Pp 41-45 (2017)
In this paper the efficiency of accelerated gradient descent methods regarding the way of determination of accelerated factor is considered. Due to the previous researches we assert that the use of Taylor's series of posed gradient descent iteration
Externí odkaz:
https://doaj.org/article/1b3f67e0fe7d40fd8da5cf31b04efa90
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Akademický článek
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