Results 61 to 70 of about 1,615 (176)
IET Smart Grid, Volume 9, Issue 1, January/December 2026.The proposed LA‐DROPF framework integrates graph neural network surrogates with Wasserstein distributionally robust optimisation and CVaR tail‐risk control for coordinated transmission—distribution dispatch under deep renewable uncertainty. A hybrid Benders—ADMM decomposition enables privacy‐preserving multi‐area coordination with formal convergence ...
Aamir Nawaz +2 more
wiley +1 more source
Distributed multiagent learning with a broadcast adaptive subgradient method
, 2010 Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function being minimized (examples of such applications include, among others ...
Yamada, I. +7 more
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On convergence properties of a subgradient method [PDF]
, 2003 In this article, we consider convergence properties of the normalized subgradient method which employs the stepsize rule based on a priori knowledge of the optimal value of the cost function.
Konnov I.
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Mathematics, 2019 Let VIP indicate the variational inequality problem with Lipschitzian and pseudomonotone operator and let CFPP denote the common fixed-point problem of an asymptotically nonexpansive mapping and a strictly pseudocontractive mapping in a real Hilbert ...
Lu-Chuan Ceng +3 more
doaj +1 more source
Journal of Advanced Transportation, Volume 2026, Issue 1, 2026.Intelligent transformation of container yards is essential for increasing terminal capacity. Demand uncertainty may lead to the risk of delays in port container operations. Traditional “predict‐then‐optimize” (PTO) frameworks often yield suboptimal results because forecasting goals are isolated from the actual decision objectives.
Xianhui Li +4 more
wiley +1 more source
Duality between subgradient and conditional gradient methods
, 2015 International audienceGiven a convex optimization problem and its dual, there are many possible first-order algorithms. In this paper, we show the equivalence between mirror descent algorithms and algorithms generalizing the conditional gradient method ...
Bach, Francis
core +1 more source
Journal of Optimization, Differential Equations and Their ApplicationsIn this paper, we introduce and study a modified inertial subgradient extragradient iterative algorithm for solving bilevel split quasimonotone variational inequality problems with a fixed point constraint of demimetric mappings in the framework of real ...
J. A. Abuchu +4 more
doaj +1 more source
Journal of Mathematics, Volume 2026, Issue 1, 2026.The goal of this study is dedicated to the research on the fixed point iterative algorithm for solving the common fixed point problems in Hilbert spaces. In this study, we first propose the hybrid and shrinking projection algorithms of two different nonexpansive mappings for a class of algorithms with two iterative sequences to solve the common fixed ...
Shengquan Weng +2 more
wiley +1 more source
Stochastic Subgradient Methods
, 2014 Stochastic subgradient methods play an important role in machine learning. We introduced the concepts of subgradient methods and stochastic subgradient methods in this project, discussed their convergence conditions as well as the strong and weak points ...
Lingjie Weng, Yutian Chen
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Exact convergence rate of the last iterate in subgradient methods
, 2023 We study the convergence of the last iterate in subgradient methods applied to the minimization of a nonsmooth convex function with bounded subgradients. We first introduce a proof technique that generalizes the standard analysis of subgradient methods.
Zamani, Moslem, Glineur, François
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