Results 71 to 80 of about 2,178 (210)
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
core
An Interior Projected-Like Subgradient Method for Mixed Variational Inequalities
Journal of Applied Mathematics, 2014 An interior projected-like subgradient method for mixed variational inequalities is proposed in finite dimensional spaces, which is based on using non-Euclidean projection-like operator. Under suitable assumptions, we prove that the sequence generated by
Guo-ji Tang, Xing Wang
doaj +1 more source
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
On convergence properties of a subgradient method
, 2020 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.
core
Efficiency of subgradient method in solving nonsmootth optimization problems [PDF]
, 2014 Nonsmooth optimization is one of the hardest type of problems to solve in optimization area. This is because of the non-differentiability of the function itself. Subgradient method is generally known as the method to solve nonsmooth optimization problems,
Abdullah, Nur Azira
core
A nonsmooth equation system solver based on subgradient method
, 2017 © 2017 IEEE. In this paper, a subgradient method is developed to solve the system of (nonsmooth) equations. First, the system of (nonsmooth) equations is transformed into a nonsmooth optimization problem with zero minimal objective function value.
Qiang Long +3 more
core +1 more source
EURO Journal on Computational OptimizationWe introduce a heuristic rule for calculating the stepsize in the subgradient method for unconstrained convex nonsmooth optimization which, unlike the classic approach, is based on retaining some information from previous iteration.
F. Carrabs, M. Gaudioso, G. Miglionico
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
``Efficient” Subgradient Methods for General Convex Optimization [PDF]
SIAM Journal on Optimization, 2016 A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of iterates is generated, which converges to within user-specified error of optimality.
openaire +3 more sources
THe Use of the Box Step Method in Discrete Optimization [PDF]
The Boxstep method is used to maximize Lagrangean functions in the context of a branch-and-bound algorithm for the general discrete optimization problem.
Roy E. Marsten
core