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Ordinal Optimization with Subset Selection Rule

Author

Listed:
  • M.S. Yang

    (Akamai Technologies)

  • L.H. Lee

    (National University of Singapore)

Abstract

Ordinal optimization (OO) has enjoyed a great degree of success in addressing stochastic optimization problems characterized by an independent and identically distributed (i.i.d.) noise. The methodology offers a statistically quantifiable avenue to find good enough solutions by means of soft computation. In this paper, we extend the OO methodology to a more general class of stochastic problems by relaxing the i.i.d. assumption on the underlying noise. Theoretical results and their applications to simple examples are presented.

Suggested Citation

  • M.S. Yang & L.H. Lee, 2002. "Ordinal Optimization with Subset Selection Rule," Journal of Optimization Theory and Applications, Springer, vol. 113(3), pages 597-620, June.
    • Handle: RePEc:spr:joptap:v:113:y:2002:i:3:d:10.1023_a:1015317022797
    DOI: 10.1023/A:1015317022797
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    References listed on IDEAS

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    1. T. W. Edward Lau & Y. C. Ho, 1997. "Universal Alignment Probabilities and Subset Selection for Ordinal Optimization," Journal of Optimization Theory and Applications, Springer, vol. 93(3), pages 455-489, June.
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    Cited by:

    1. Yang, Mike S. & Lee, Loo Hay, 2006. "An illustrative case study on application of learning based ordinal optimization approach to complex deterministic problem," European Journal of Operational Research, Elsevier, vol. 174(1), pages 265-277, October.

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    More about this item

    Keywords

    Ordinal optimization; goal softening;

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