A bundle method for nonsmooth DC programming with application to chance-constrained problems
From MaRDI portal
Publication:2028493
DOI10.1007/s10589-020-00241-8zbMath1469.90115OpenAlexW3110176297MaRDI QIDQ2028493
Sophie Demassey, Welington de Oliveira, P. Javal, Wim van Ackooij, B. Swaminathan, H. Morais
Publication date: 1 June 2021
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-020-00241-8
Related Items (7)
DC semidefinite programming and cone constrained DC optimization. I: Theory ⋮ Nonconvex and nonsmooth approaches for affine chance-constrained stochastic programs ⋮ Probability maximization via Minkowski functionals: convex representations and tractable resolution ⋮ Steering exact penalty DCA for nonsmooth DC optimisation problems with equality and inequality constraints ⋮ A bundle-type method for nonsmooth DC programs ⋮ Retraction-based first-order feasible methods for difference-of-convex programs with smooth inequality and simple geometric constraints ⋮ A derivative-free trust-region algorithm with copula-based models for probability maximization problems
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On the quantification of nomination feasibility in stationary gas networks with random load
- Level bundle methods for constrained convex optimization with various oracles
- Bundle-based descent method for nonsmooth multiobjective DC optimization with inequality constraints
- A proximal difference-of-convex algorithm with extrapolation
- DC programming and DCA: thirty years of developments
- Accelerating the DC algorithm for smooth functions
- Minimizing nonsmooth DC functions via successive DC piecewise-affine approximations
- A joint model of probabilistic/robust constraints for gas transport management in stationary networks
- Convergence of non-smooth descent methods using the Kurdyka-Łojasiewicz inequality
- Proximal bundle methods for nonsmooth DC programming
- An inertial algorithm for DC programming
- A local search method for optimization problem with d.c. inequality constraints
- A general double-proximal gradient algorithm for d.c. programming
- Variations and extension of the convex-concave procedure
- Inexact stabilized Benders' decomposition approaches with application to chance-constrained problems with finite support
- Computing B-Stationary Points of Nonsmooth DC Programs
- Convergence analysis of a proximal point algorithm for minimizing differences of functions
- Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Constrained Bundle Methods for Upper Inexact Oracles with Application to Joint Chance Constrained Energy Problems
- Mixed $H_2/H_\infty$ Control via Nonsmooth Optimization
- Double Bundle Method for finding Clarke Stationary Points in Nonsmooth DC Programming
- (Sub-)Gradient Formulae for Probability Functions of Random Inequality Systems under Gaussian Distribution
- Non-smooth DC-constrained optimization: constraint qualification and minimizing methodologies
- Convex analysis and global optimization
- Benchmarking optimization software with performance profiles.
- A proximal bundle method for nonsmooth nonconvex functions with inexact information
This page was built for publication: A bundle method for nonsmooth DC programming with application to chance-constrained problems
