Optimum Seeking Methods (several variables)

  • Loo-Keng Hua
  • Yuan Wang
  • J. G. C. Heijmans
Part of the Mathematical Modeling book series (MMO, volume 2)


For simplicity, our discussion is confined to the case of two variables only. Most of the optimum seeking methods for two variables can be easily generalized to the case of more than two variables, but the number of required trials grows very fast when the number of variables is increased. Thus the use of optimum seeking methods for several variables should be avoided as much as possible, even though there are always many factors that influence an industrial production process. It is better to grasp the principal factors and to use an optimum seeking method on one or two variables so as to obtain a better production technology for these factors, and then use the same method to improve the production technologies with respect to the next one or two factors, and so on, until a satisfactory result is obtained. It seems that this is more reliable and reasonable than the direct use of an optimum seeking method for several variables. We refer back to section 3.1 for some of the real problems we have tackled.


Convex Body Maximum Point Equilateral Triangle Unimodal Function Bisection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Hua Loo Keng. Theory of Optimization. Science Press, Beijing, 1981.Google Scholar
  2. Hua Loo Keng and Wang Yuan. Applications of Number Theory to Numerical Analysis. Science Press, Beijing, 1978 and Springer Verlag, 1981.Google Scholar

Copyright information

© Birkhäuser Boston 1989

Authors and Affiliations

  • Loo-Keng Hua
  • Yuan Wang
    • 1
  • J. G. C. Heijmans
    • 2
  1. 1.Academia SinicaInstitute of MathematicsBeijingPeople’s Republic of China
  2. 2.Department of MathematicsUniversity of Texas at ArlingtonArlingtonUSA

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