Sensitivity Analysis for Convex Multiobjective Programming in Abstract Spaces
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TL;DR: In this article, it was shown that for a linear or convex multiobjective program, a dual program can be obtained which gives the primal sensitivity without any special hypothesis about the way of choosing the optimal solution in the efficient set.
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About: This article is published in Journal of Mathematical Analysis and Applications. The article was published on 01 Sep 1996. and is currently open access. The article focuses on the topics: Sensitivity (control systems).
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Citations
Linear Programming in Infinite-Dimensional Spaces
TL;DR: In this article, the authors present linear programming in infinite-dimensional spaces and show that linear programming can be used to solve problems in the real world as well as in the virtual world.
190
Sensitivity Analysis in MCDM
Tetsuzo Tanino
- 01 Jan 1999
TL;DR: This chapter will explain several approaches, though limited, to stability and sensitivity analysis in MCDM.
33
Nonlinear Multiobjective Programming
Tetsuzo Tanino,Hun Kuk +1 more
- 01 Jan 2003
TL;DR: In this paper, the authors provide an annotated bibliography of nonlinear multiobjective programming problems, including scalarization techniques and optimality conditions for nonlinear MOP problems.
19
Duality of nonscalarized multiobjective linear programs: dual balance, level sets, and dual clusters of optimal vectors
TL;DR: A new concept of duality is proposed for multiobjective linear programs based on a set expansion process for the computation of optimal solutions without scalarization that presents a cluster of optimal dual vectors that corresponds to a unique optimal primal vector.
15
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David G. Luenberger
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Linear multiobjective programming
Milan Zeleny
- 01 Jan 1974
TL;DR: The Origin of the Multiobjective Problem and a Short Historical Review are reviewed and a method for Generating Adjacent Extreme Points - A Second Approach (Multicriteria Simplex Method).
607
Linear Programming in Infinite-Dimensional Spaces
TL;DR: In this article, the authors present linear programming in infinite-dimensional spaces and show that linear programming can be used to solve problems in the real world as well as in the virtual world.
190