Vector optimization and generalized Lagrangian duality
type: Item
instance of: scholarly article
title: Vector optimization and generalized Lagrangian duality
zbMATH Open document ID: 0822.90119
DOI: 10.1007/BF02032078
author: Matthew L. TenHuisen
author: Malgorzata M. Wiecek
published in: Annals of Operations Research
publication date: 1994-12-01T00:00:00Z
review text: For a not necessarily convex vector optimization problem in a finite- dimensional setting a Lagrange duality theory is developed using a Lagrangian function for which the multipliers are not constants but functions. The approach is based on an \(\varepsilon\)-constrained scalarization technique.
reviewed by: Johannes Jahn
Mathematics Subject Classification ID: 90C29
zbMATH DE Number: 698964
zbMATH Keywords: convex vector optimization
zbMATH Keywords: Lagrange duality
zbMATH Keywords: \(\varepsilon\)-constrained scalarization technique
MaRDI profile type: Publication
cites work: Q3998346
cites work: Application of multiobjective optimization in aircraft control systems design
cites work: Proper efficiency and the theory of vector maximization
cites work: Q3760254
cites work: Existence of efficient solutions for vector maximization problems
cites work: On the completeness and constructiveness of parametric characterizations to vector optimization problems
cites work: An interactive weighted Tchebycheff procedure for multiple objective programming
cites work: Q4721876
cites work: Multiple-objective programming with polynomial objectives and constraints
cites work: Use of exact penalty functions to determine efficient decisions
cites work: Q5613727
cites work: Generalized Penalty-Function Concepts in Mathematical Optimization
cites work: Extensions of Lagrange Multipliers in Nonlinear Programming
cites work: Geometry and resolution of duality gaps
cites work: A generalized Lagrangian function and multiplier method
full work available at URL: https://doi.org/10.1007/bf02032078
OpenAlex ID: W1984370382
description: scientific article; zbMATH DE number 698964