Mathematical Programming

, Volume 67, Issue 1–3, pp 77–88 | Cite as

A saddle-point characterization of Pareto optima

  • M. van Rooyen
  • X. Zhou
  • S. Zlobec
Article

Abstract

This paper provides an answer to the following basic problem of convex multi-objective optimization: Find a saddle-point condition that is both necessary and sufficient that a given point be Pareto optimal. No regularity condition is assumed for the constraints or the objectives.

AMS Subject Classification

90C25 90C29 

Keywords

Welfare economics Pareto optima Saddle point Lagrangian function 

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References

  1. [1]
    R.A. Abrams and L. Kerzner, “A simplified test for optimality,”Journal of Optimization Theory and Applications 25 (1978) 161–170.Google Scholar
  2. [2]
    K.J. Arrows, “An extension of the basic theorems of classical welfare economics,”Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, J. Neyman, editor (University of California Press, Berkeley, California, 1951).Google Scholar
  3. [3]
    A. Ben-Israel, A. Ben-Tal and A. Charnes, “Necessary and sufficient conditions for a Pareto optimum in convex programming,”Econometrica 45 (1977) 811–820.Google Scholar
  4. [4]
    A. Ben-Israel, A. Ben-Tal and S. Zlobec,Optimality in Nonlinear Programming: A Feasible Directions Approach (Wiley-Interscience, New York, 1981).Google Scholar
  5. [5]
    A. Charnes and W.W. Cooper,Management Models and Industrial Applications of Linear Programming, Vol. 1 (Wiley, New York, 1961).Google Scholar
  6. [6]
    G. Debreu,Theory of Value, An Axiomatic Analysis of Economic Equilibrium (Wiley, New York, 1959).Google Scholar
  7. [7]
    A.M. Geoffrion, “Proper efficiency and the theory of vector maximization,”Journal of Mathematical Analysis and Applications 42 (1968) 618–630.Google Scholar
  8. [8]
    M.D. Intriligator,Mathematical Optimization (Prentice-Hall, Englewood Cliffs, 1971).Google Scholar
  9. [9]
    S. Karlin,Mathematical Methods and Theory in Games, Programming and Economics, Vol. 1 (Addison-Wesley, Reading, Mass., 1959).Google Scholar
  10. [10]
    M.E. Salukvadze,Vector-Valued Optimization Problems in Control Theory (Academic, New York, 1979).Google Scholar
  11. [11]
    S. Smale, “Global analysis and economics III,”Journal of Mathematical Economics 1 (1974) 107–117.Google Scholar
  12. [12]
    S. Smale, “Global analysis and economics VI,”Journal of Mathematical Economics 3 (1976) 1–14.Google Scholar
  13. [13]
    J. Valyi, “Approximate saddle-point theorems in vector optimization,”Journal of Optimization Theory and Applications 55 (1987) 435–448.Google Scholar
  14. [14]
    T.L. Vincent and W.J. Grantham,Optimality in Parametric Systems (Wiley-Interscience, New York, 1981).Google Scholar
  15. [15]
    P.L. Yu, “Cone convexity, cone extreme points, and nondominated solutions in decision problems with multiobjectives,” in:Multicriteria Decision Making and Differential games, G. Leitmann, editor (Plenum, New York, 1976).Google Scholar
  16. [16]
    X. Zhou, F. Sharifi Mokhtarian and S. Zlobec, “A simple constraint qualification in convex programming,”Mathematical Programming 61 (3) (1993) 385–397.Google Scholar
  17. [17]
    S. Zlobec, “Two characterizations of Pareto minima in convex multicriteria optimization,”Aplikace Matematiky 29 (1984) 342–349.Google Scholar
  18. [18]
    S. Zlobec, “Characterizing optimality in mathematical programming models,”Acta Applicandae Mathematicae 12 (1988) 112–180.Google Scholar

Copyright information

© The Mathematical Programming Society, Inc 1994

Authors and Affiliations

  • M. van Rooyen
    • 1
  • X. Zhou
    • 2
  • S. Zlobec
    • 2
  1. 1.Department of Computational and Applied MathematicsUniversity of the WitwatersrandJohannesburgSouth Africa
  2. 2.Department of Mathematics and StatisticsMcGill UniversityMontrealCanada

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