Results 61 to 70 of about 22,536 (196)

Subgradient projection algorithm, II

Journal of Approximation Theory, 1984
The method presented by the author in Part I [ibid. 35, 111-126 (1982; Zbl 0486.65042)] for the minimization of certain nondifferentiable functions constrained to a convex polytope is extended to the minimization of a strictly convex piecewise smooth function subject to smooth convex constraints.
openaire   +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

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

A new subgradient extragradient method for solving the split modified system of variational inequality problems and fixed point problem

Journal of Inequalities and Applications, 2022
We introduce a new subgradient extragradient algorithm utilizing the concept of the set of solutions of the split modified system of variational inequality problems (SMSVIP).
Anchalee Sripattanet, Atid Kangtunyakarn
doaj   +1 more source

A subgradient projection algorithm

Journal of Approximation Theory, 1982
AbstractUsing only certain easily computable ε-subgradients, an implementable convergent algorithm for finding the minimizer of a non-differentiable objective function subject to a finite number of linear constraints in d-dimensional space is given. The particular objective function consists of the pointwise maximum of a finite system of pseudoconvex ...
openaire   +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

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  

Delayed Star Subgradient Methods for Constrained Nondifferentiable Quasi-Convex Optimization

Algorithms
In this work, we consider the problem of minimizing a quasi-convex function over a nonempty closed convex constrained set. In order to approximate a solution of the considered problem, we propose delayed star subgradient methods.
Ontima Pankoon, Nimit Nimana
doaj   +1 more source

New Variations and Structural Refinements of Discrete Weighted Jensen and Hermite–Hadamard Inequalities Using (α, m)‐Convex Mappings

Journal of Mathematics, Volume 2026, Issue 1, 2026.
This article develops new Hermite–Hadamard and Jensen‐type inequalities for the class of (α, m)‐convex functions. New product forms of Hermite–Hadamard inequalities are established, covering multiple distinct scenarios. Several nontrivial examples and remarks illustrate the sharpness of these results and demonstrate how earlier inequalities can be ...
Shama Firdous, Waqas Nazeer, Waqar Afzal, Joshua Kiddy K. Asamoah, Hadeel AlQadi, Pramita Mishra   +5 more
wiley   +1 more source

Tight analyses for subgradient descent I: Lower bounds

Open Journal of Mathematical Optimization
Consider the problem of minimizing functions that are Lipschitz and convex, but not necessarily differentiable. We construct a function from this class for which the $Tþ$ iterate of subgradient descent has error $\Omega (\log (T)/\sqrt{T})$. This matches
Harvey, Nicholas J. A., Liaw, Chris, Randhawa, Sikander   +2 more
doaj   +1 more source