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A Discussion of Random Methods for Seeking Maxima

Author

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  • Samuel H. Brooks

    (Technical Operations, Incorporated, Monterey, California)

Abstract

In the design of experiments for the purpose of seeking maxima, random methods are shown to have an important place in the consideration of the experimenter. For a rather large class of experimental situations, an elementary probability formulation leads to an exact statement for the number of trials required in the experiment. The rationale, application, and relative merits of random methods are discussed.

Suggested Citation

  • Samuel H. Brooks, 1958. "A Discussion of Random Methods for Seeking Maxima," Operations Research, INFORMS, vol. 6(2), pages 244-251, April.
    • Handle: RePEc:inm:oropre:v:6:y:1958:i:2:p:244-251
    DOI: 10.1287/opre.6.2.244
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    Cited by:

    1. Giulia Pedrielli & K. Selcuk Candan & Xilun Chen & Logan Mathesen & Alireza Inanalouganji & Jie Xu & Chun-Hung Chen & Loo Hay Lee, 2019. "Generalized Ordinal Learning Framework (GOLF) for Decision Making with Future Simulated Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-35, December.
    2. Boehlje, M. D. & Eisgruber, L. M., 1975. "A Decision Model For The Estate Management Problem," ISU General Staff Papers 197508010700001017, Iowa State University, Department of Economics.
    3. Kyung-Yong Lee & Jung-Sung Park & Yun-Su Kim, 2021. "Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network," Energies, MDPI, vol. 14(22), pages 1-18, November.
    4. David E. Kaufman & Robert L. Smith, 1998. "Direction Choice for Accelerated Convergence in Hit-and-Run Sampling," Operations Research, INFORMS, vol. 46(1), pages 84-95, February.
    5. D. Bulger & W. P. Baritompa & G. R. Wood, 2003. "Implementing Pure Adaptive Search with Grover's Quantum Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 116(3), pages 517-529, March.
    6. Rinnooy Kan, A. H. G. & Timmer, G. T., 1985. "Stochastic Global Optimization Methods Part I: Clustering Methods," Econometric Institute Archives 272329, Erasmus University Rotterdam.
    7. Zheng Peng & Donghua Wu & Wenxing Zhu, 2016. "The robust constant and its applications in random global search for unconstrained global optimization," Journal of Global Optimization, Springer, vol. 64(3), pages 469-482, March.
    8. Barkema, Alan Dean, 1985. "Farm survival under uncertainty," ISU General Staff Papers 1985010108000017535, Iowa State University, Department of Economics.
    9. Alok Shukla & Prakash Vedula, 2019. "Trajectory optimization using quantum computing," Journal of Global Optimization, Springer, vol. 75(1), pages 199-225, September.
    10. Taymaz, Erol, 1993. "A Calibration Algorithm for Micro-Simulation Models," Working Paper Series 374, Research Institute of Industrial Economics.
    11. E. A. Tsvetkov & R. A. Krymov, 2022. "Pure Random Search with Virtual Extension of Feasible Region," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 575-595, November.
    12. D. W. Bulger, 2007. "Combining a Local Search and Grover’s Algorithm in Black-Box Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 133(3), pages 289-301, June.

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