Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/11340
Nаziv: | Descent direction stochastic approximation algorithm with adaptive step sizes | Аutоri: | Lužanin, Zorana Stojkovska I. Kresoja M. |
Dаtum izdаvаnjа: | 1-јан-2019 | Čаsоpis: | Journal of Computational Mathematics | Sažetak: | © 2019 Global Science Press. All rights reserved. A stochastic approximation (SA) algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed. New adaptive step size scheme uses ordered statistics of fixed number of previous noisy function values as a criterion for accepting good and rejecting bad steps. The scheme allows the algorithm to move in bigger steps and avoid steps proportional to 1/k when it is expected that larger steps will improve the performance. An algorithm with the new adaptive scheme is defined for a general descent direction. The almost sure convergence is established. The performance of new algorithm is tested on a set of standard test problems and compared with relevant algorithms. Numerical results support theoretical expectations and verify efficiency of the algorithm regardless of chosen search direction and noise level. Numerical results on problems arising in machine learning are also presented. Linear regression problem is considered using real data set. The results suggest that the proposed algorithm shows promise. | URI: | https://open.uns.ac.rs/handle/123456789/11340 | ISSN: | 02549409 | DOI: | 10.4208/jcm.1710-m2017-0021 |
Nаlаzi sе u kоlеkciјаmа: | PMF Publikacije/Publications |
Prikаzаti cеlоkupаn zаpis stаvki
SCOPUSTM
Nаvоđеnjа
1
prоvеrеnо 14.09.2022.
Prеglеd/i stаnicа
29
Prоtеklа nеdеljа
13
13
Prоtеkli mеsеc
0
0
prоvеrеnо 10.05.2024.
Google ScholarTM
Prоvеritе
Аlt mеtrikа
Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.