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Optimal Sequential Sampling Policy of Partitioned Random Search and Its Approximation

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  • Z. B. Tang

    (Hong Kong University of Science and Technology)

Abstract

The paper studies the optimal sequential sampling policy of the partitioned random search (PRS) and its approximation. The PRS is a recently proposed approach for function optimization. It takes explicitly into consideration computation time or cost, assuming that there exist both a cost for each function evaluation and a finite total computation time constraint. It is also motivated at improving efficiency of the widely used crude random search. In particular, the PRS considers partitioning the search region of an objective function into K subregions and employing an independent and identically distributed random sampling scheme for each of K subregions. A sampling policy decides when to terminate the sampling process or which subregion to be sampled next.

Suggested Citation

  • Z. B. Tang, 1998. "Optimal Sequential Sampling Policy of Partitioned Random Search and Its Approximation," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 431-448, August.
    • Handle: RePEc:spr:joptap:v:98:y:1998:i:2:d:10.1023_a:1022645702900
    DOI: 10.1023/A:1022645702900
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    References listed on IDEAS

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    1. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
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    Cited by:

    1. H. Q. Ye & Z. B. Tang, 2001. "Partitioned Random Search for Global Optimization with Sampling Cost and Discounting Factor," Journal of Optimization Theory and Applications, Springer, vol. 110(2), pages 445-455, August.

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