Results 71 to 80 of about 19,600 (195)

A Distributed Newton Method for Network Utility Maximization

, 2010
Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of convergence properties.
Jadbabaie, Ali, Ozdaglar, Asuman, Wei, Ermin   +2 more
core   +3 more sources

Regret Function Minimization Algorithms

Кібернетика та комп'ютерні технології
Introduction. The article addresses the problem of making optimal decisions under uncertainty by minimizing the Savage regret function. This function, which evaluates the difference between the actual outcome and the best possible outcome across all ...
Anatolie Baractari, Borys Chumakov, Anatol Godonoaga   +2 more
doaj   +1 more source

Distributed Model Predictive Control of Microgrids: A Review on Recent Developments

IET Control Theory &Applications, Volume 20, Issue 1, January/December 2026.
This paper presents a comprehensive review of distributed model predictive control (DMPC) for microgrids, synthesizing recent developments from the past decade. The review categorizes DMPC implementations by communication architectures, control challenges, and algorithmic strategies, while evaluating their performance in terms of computational burden ...
Hossein G. Sahebi, Mojtaba Hajihosseini, Saman A. Gorji   +2 more
wiley   +1 more source

RESEARCH OF ONE VARIANT OF SUBGRADIENT METHOD

Вестник Кемеровского государственного университета, 2015
The subgradient step selection method based on the known minimal value of function is studied in the paper. The authors show that it is an analogue of the method of minimal errors for solving linear equation systems.
N. S. Samoylenko, V. N. Krutikov, V. V. Meshechkin   +2 more
doaj  

Optimal Sparse Array Design Against Desired Signal DOA Mismatch for Robust Adaptive Beamforming

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
The performance of the adaptive beamformer is not only related to the array weight but also influenced by the array structure. Different sparse array configurations exhibit varying sensitivities to uncertainties in the signal direction of arrival (DOA).
Weinian Li, Lichun Li, Chaoyue Song, Zhengyang Xu, Jingna Jiao, Wenbo Zhang   +5 more
wiley   +1 more source

Newtonian Property of Subgradient Method with Optimization of Metric Matrix Parameter Correction

Mathematics
The work proves that under conditions of instability of the second derivatives of the function in the minimization region, the estimate of the convergence rate of Newton’s method is determined by the parameters of the irreducible part of the ...
Elena Tovbis, Vladimir Krutikov, Lev Kazakovtsev   +2 more
doaj   +1 more source

Operational Properties of SCN Function, Optimization Condition, and Exactness of Penalty Function for SCN Optimization

Journal of Mathematics, Volume 2026, Issue 1, 2026.
This paper defines a strong convertible nonconvex (SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth (nondifferentiable) function. First, the concept of SCN function is defined, where the SCN functions are nonconvex or nonsmooth.
Min Jiang, Chuangyin Dang, Zhiqing Meng, Rui Shen, Zai Yun Peng   +4 more
wiley   +1 more source

Determining the Particle Size of Pulverized Coal From Spectral Reflectivity Measurements

Journal of Spectroscopy, Volume 2026, Issue 1, 2026.
Pulverized coal is widely used in industrial energy supplies and chemical production. Its combustion efficiency and reactivity are closely related to its particle size distribution. Therefore, precise, fast, and reliable measurement methods are required to determine the particle size of pulverized coal.
Chengkun Wang, Kaiwen Zong, Yitong Xu, Songhao Li, Bohong Liu, Qiuduo Zhao, Khalique Ahmed   +6 more
wiley   +1 more source

Incremental and Parallel Machine Learning Algorithms With Automated Learning Rate Adjustments

Frontiers in Robotics and AI, 2019
The existing machine learning algorithms for minimizing the convex function over a closed convex set suffer from slow convergence because their learning rates must be determined before running them.
Kazuhiro Hishinuma, Hideaki Iiduka
doaj   +1 more source

Drayage Routing Problems: A Comprehensive Survey and a New Compact Model

Australian &New Zealand Journal of Statistics, Volume 67, Issue 4, Page 491-526, December 2025.
ABSTRACT Drayage involves short‐distance container trucking, a crucial part of intermodal transportation that fills the gap between long‐haul transportation modes and inland facilities. Because of its high costs, drayage has been increasingly drawing the attention of researchers and practitioners from different disciplines, who have been exploring ...
Daniel Bustos‐Coral, Alysson M. Costa
wiley   +1 more source