Level bundle-like algorithms for convex optimization
From MaRDI portal
Publication:743968
DOI10.1007/s10898-013-0096-4zbMath1330.90073OpenAlexW2129247866MaRDI QIDQ743968
José Yunier Bello Cruz, Welington de Oliveira
Publication date: 2 October 2014
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-013-0096-4
Related Items (7)
A relaxed-projection splitting algorithm for variational inequalities in Hilbert spaces ⋮ Level bundle methods for constrained convex optimization with various oracles ⋮ Acceleration techniques for level bundle methods in weakly smooth convex constrained optimization ⋮ On Weak and Strong Convergence of the Projected Gradient Method for Convex Optimization in Real Hilbert Spaces ⋮ Nonsmooth and nonconvex optimization via approximate difference-of-convex decompositions ⋮ Adaptive Sequential Sample Average Approximation for Solving Two-Stage Stochastic Linear Programs ⋮ A doubly stabilized bundle method for nonsmooth convex optimization
Cites Work
- Unnamed Item
- Approximate level method for nonsmooth convex minimization
- On the choice of step size in subgradient optimization
- Variable target value subgradient method
- On the projected subgradient method for nonsmooth convex optimization in a Hilbert space
- A branch and bound method for stochastic global optimization
- Monte Carlo bounding techniques for determinig solution quality in stochastic programs
- Bundle-type methods for inexact data
- Non-Euclidean restricted memory level method for large-scale convex optimization
- A descent proximal level bundle method for convex nondifferentiable optimization
- Proximal level bundle methods for convex nondifferentiable optimization, saddle-point problems and variational inequalities
- New variants of bundle methods
- A computational study of a solver system for processing two-stage stochastic LPs with enhanced Benders decomposition
- Level bundle methods for oracles with on-demand accuracy
- Inexact Bundle Methods for Two-Stage Stochastic Programming
- A Strongly Convergent Method for Nonsmooth Convex Minimization in Hilbert Spaces
- Lectures on Stochastic Programming
- Monte Carlo sampling approach to stochastic programming
- Minimization of unsmooth functionals
- Benchmarking optimization software with performance profiles.
- Extension of subgradient techniques for nonsmooth optimization in Banach spaces
This page was built for publication: Level bundle-like algorithms for convex optimization
