Results 91 to 100 of about 24,030 (229)

A two-point heuristic to calculate the stepsize in subgradient method with application to a network design problem

EURO Journal on Computational Optimization
We 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

Randomized Coordinate Subgradient Method for Nonsmooth Composite Optimization [PDF]

, 2022
Lei Zhao, Ding Chen, Daoli Zhu, Xiao Li
openalex   +1 more source

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

A new constraint qualification for optimality of nonconvex nonsmooth optimization problems

, 2022
Fatemeh Bazargan, H. Mohebi
openalex   +2 more sources

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems [PDF]

, 2022
Atara Kaplan, Dan Garber
openalex   +1 more source

Variational Optimality Conditions with Feedback Descent Controls that Strengthen the Maximum Principle

Известия Иркутского государственного университета: Серия "Математика", 2014
We derive nonlocal necessary optimality conditions that strengthen both classical and nonsmooth Maximum Principles for nonlinear optimal control problems with free right-hand end of trajectories.
V.A. Dykhta
doaj  

Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold [PDF]

, 2020
Bokun Wang, Shiqian Ma, Lingzhou Xue
openalex   +1 more source

The NFDA-Nonsmooth Feasible Directions Algorithm applied to construction of Pareto Fronts of Ridge and Lasso Regressions

Trends in Computational and Applied Mathematics
Ridge and Lasso regressions are types of linear regression, a machine learning tool for dealing with data. Based on multiobjective optimization theory, we transform Ridge and Lasso regression into bi-objective optimization problems. The Pareto fronts of
W. P. Freire
doaj   +1 more source

On a Monotone Scheme for Nonconvex Nonsmooth Optimization with Applications to Fracture Mechanics. [PDF]

J Optim Theory Appl, 2019
Ghilli D, Ghilli D, Kunisch K.
europepmc   +1 more source

An Inertial Block Majorization Minimization Framework for Nonsmooth\n Nonconvex Optimization [PDF]

, 2020
Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis   +2 more
openalex   +1 more source