Applying convexificators in robust multiobjective optimization

DOI10.3934/jimo.2023087OpenAlexW4383563412MaRDI QIDQ6086987

Nazih Abderrazzak Gadhi, Mohamed Ohda

Publication date: 11 December 2023

Published in: Journal of Industrial and Management Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/jimo.2023087

zbMATH Keywords

optimality conditionsrobust multiobjective optimizationconvexifactorsubconvexlike mapsweakly robust efficient solutions

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Multi-objective and goal programming (90C29) Nonlinear programming (90C30) Optimality conditions and duality in mathematical programming (90C46)

Cites Work

• Unnamed Item
• Unnamed Item
• Some characterizations of robust optimal solutions for uncertain convex optimization problems
• Robust duality for generalized convex programming problems under data uncertainty
• Convexifactors, generalized convexity, and optimality conditions
• Subsmooth semi-infinite and infinite optimization problems
• Necessary optimality conditions for bilevel set optimization problems
• A generalization of a minimax theorem of Fan via a theorem of the alternative
• Nonsmooth calculus, minimality, and monotonicity of convexificators
• Necessary and sufficient conditions for Pareto efficiency in robust multiobjective optimization
• On classes of generalized convex functions, Gordan-Farkas type theorems, and Lagrangean duality
• Connectedness of the efficient point sets in quasiconcave vector maximization
• Linear programming with uncertain data: some extensions to robust optimization
• Characterizing robust solution sets of convex programs under data uncertainty
• Necessary optimality conditions in terms of convexificators in Lipschitz optimization
• Lipschitz behavior of solutions to nonconvex semi-infinite vector optimization problems
• Optimality conditions and duality for robust nonsmooth multiobjective optimization problems with constraints
• On robust multiobjective optimization
• Nonsmooth vector functions and continuous optimization
• Tractable approximations to robust conic optimization problems
• Necessary optimality conditions for bilevel optimization problems using convexificators
• On the continuity of the minimum set of a continuous function
• Optimization and nonsmooth analysis
• Scalarization and lagrange duality in multiobjective optimization
• Convexifactors, generalized convexity and vector optimization
• A characterization of quasiconvex vector-valued functions
• What is quasiconvex analysis?
• Improved enhanced Fritz John condition and constraints qualifications using convexificators
• Subdifferentials of Nonconvex Supremum Functions and Their Applications to Semi-infinite and Infinite Programs with Lipschitzian Data
• ON NONSMOOTH OPTIMALITY THEOREMS FOR ROBUST OPTIMIZATION PROBLEMS
• Set-valued Optimization
• Optimality and duality for robust multiobjective optimization problems

This page was built for publication: Applying convexificators in robust multiobjective optimization