Inverse optimization for multi-objective linear programming

This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with re...

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Vydáno v:Optimization letters Ročník 13; číslo 2; s. 281 - 294
Hlavní autoři: Naghavi, Mostafa, Foroughi, Ali Asghar, Zarepisheh, Masoud
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2019
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ISSN:1862-4472, 1862-4480
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Abstract This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with regular “optimality” replaced by the “Pareto optimality”. This extension, however, leads to a non-convex optimization problem. We prove some special characteristics of the problem, allowing us to solve the non-convex problem by solving a series of convex problems.
AbstractList This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with regular "optimality" replaced by the "Pareto optimality". This extension, however, leads to a non-convex optimization problem. We prove some special characteristics of the problem, allowing us to solve the non-convex problem by solving a series of convex problems.
This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with regular "optimality" replaced by the "Pareto optimality". This extension, however, leads to a non-convex optimization problem. We prove some special characteristics of the problem, allowing us to solve the non-convex problem by solving a series of convex problems.This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given feasible solution a weak efficient solution. This is a natural extension of inverse optimization for single-objective linear programming with regular "optimality" replaced by the "Pareto optimality". This extension, however, leads to a non-convex optimization problem. We prove some special characteristics of the problem, allowing us to solve the non-convex problem by solving a series of convex problems.
Author Zarepisheh, Masoud
Foroughi, Ali Asghar
Naghavi, Mostafa
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  fullname: Zarepisheh, Masoud
  organization: Department of Medical Physics, Memorial Sloan Kettering Cancer Center
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Keywords Linear programming
Inverse optimization
Efficiency
Multi-objective linear programming
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  doi: 10.1016/S0166-218X(01)00261-X
– volume: 53
  start-page: 45
  issue: 1
  year: 1992
  ident: 1394_CR6
  publication-title: Math. Program.
  doi: 10.1007/BF01585693
– volume-title: Multiobjective Linear Programming
  year: 2016
  ident: 1394_CR15
  doi: 10.1007/978-3-319-21091-9
– volume-title: Convex Optimization
  year: 2004
  ident: 1394_CR5
  doi: 10.1017/CBO9780511804441
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Snippet This paper generalizes inverse optimization for multi-objective linear programming where we are looking for the least problem modifications to make a given...
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SubjectTerms Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
Title Inverse optimization for multi-objective linear programming
URI https://link.springer.com/article/10.1007/s11590-019-01394-0
https://www.ncbi.nlm.nih.gov/pubmed/35685276
https://www.proquest.com/docview/2675605892
https://pubmed.ncbi.nlm.nih.gov/PMC9175571
Volume 13
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