Zobrazeno 1 - 10
of 6 221
pro vyhledávání: '"Majorization-Minimization"'
Publikováno v:
Foundations and Trends in Signal Processing 2024: Vol. 18: No. 4, pp 310-389
This monograph presents a theoretical background and a broad introduction to the Min-Max Framework for Majorization-Minimization (MM4MM), an algorithmic methodology for solving minimization problems by formulating them as min-max problems and then em
Externí odkaz:
http://arxiv.org/abs/2411.07561
In this paper, we present a novel optimization algorithm designed specifically for estimating state-space models to deal with heavy-tailed measurement noise and constraints. Our algorithm addresses two significant limitations found in existing approa
Externí odkaz:
http://arxiv.org/abs/2411.11320
In this paper, we develop a unified framework and convergence analysis of first-order methods for vector optimization problems (VOPs) from a majorization-minimization perspective. By selecting different surrogate functions, the general method can be
Externí odkaz:
http://arxiv.org/abs/2407.13245
Nonnegative matrix factorization (NMF) is a popular method in machine learning and signal processing to decompose a given nonnegative matrix into two nonnegative matrices. In this paper, to solve NMF, we propose new algorithms, called majorization-mi
Externí odkaz:
http://arxiv.org/abs/2405.11185
Publikováno v:
In European Journal of Operational Research 1 December 2024 319(2):494-504
Publikováno v:
In Neural Networks November 2024 179
Akademický článek
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Autor:
Chen, Yuxue1 (AUTHOR) chyuxue@hotmail.com, Zhou, Shuisheng1 (AUTHOR) sszhou@mail.xidian.edu.cn
Publikováno v:
Entropy. Aug2024, Vol. 26 Issue 8, p670. 21p.
Hyperparameter tuning is an important task of machine learning, which can be formulated as a bilevel program (BLP). However, most existing algorithms are not applicable for BLP with non-smooth lower-level problems. To address this, we propose a singl
Externí odkaz:
http://arxiv.org/abs/2403.00314