Paper Info
Title
Saddle Points and Pareto Points in Multiple Objective Programming
Abstract
In this paper relationships between Pareto points and saddle points are studied in convex and nonconvex multiple objective programming. The analysis is based on partitioning the index sets of objectives and constraints and splitting the original problem into subproblems having a special structure. The results are based on scalarizations of multiple objective programs and related linear and augmented Lagrangian functions. In the nonconvex case, a saddle point characterization of Pareto points is possible under assumptions that guarantee existence of Pareto points and stability conditions of single objective problems. Essentially, these conditions are not stronger than those in analogous results for single objective programming.
Year
DOI
Venue
2005
10.1007/s10898-004-5902-6
J. Global Optimization
Keywords
DocType
Volume
nonconvex multiple objective programming,nonconvex case,saddle points,multiple objective programming,pareto points,saddle point characterization,analogous result,saddle point,augmented lagrangian function,lagrangian functions,pareto point,multiple objective programs,single objective programming,single objective problem,multiple objective program,augmented lagrangian,indexation
Journal
32
Issue
ISSN
Citations 
1
0925-5001
3
PageRank 
References 
Authors
0.56
4
2
Name
Order
Citations
PageRank
Matthias Ehrgott192360.59
Margaret M. Wiecek221322.90