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Global Optimization

  • Wilhelm Forst
  • Dieter Hoffmann
Chapter
Part of the Springer Undergraduate Texts in Mathematics and Technology book series (SUMAT)

Abstarct

Global optimization is concerned with the computation and characterization of global optimizers of — in general — nonlinear functions. It is an important task since many real-world questions lead to global rather than local problems. Global optimization has a variety of applications including, for example, chemical process design, chip layout, planning of just-in-time manufacturing, and pooling and blending problems.

Keywords

Global Optimization Global Minimum Extreme Point Feasible Point Convex Envelope 
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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References

  1. Ho/Pa.
    R. Horst, P. M. Pardalos (eds.) (1995): Handbook of Global Optimization Vol. 1. Kluwer, Boston, Dordrecht, LondonGoogle Scholar
  2. Lan.
    S. Lang (1972): Linear Algebra. Addison-Wesley, ReadingGoogle Scholar
  3. Fa/Ho.
    J. E. Falk, K. R. Hoffman (1976): A Successive Underestimation Method for Concave Minimization Problems. Math. Oper. Research 1, pp. 251–259Google Scholar
  4. Fa/So.
    J. E. Falk, R. M. Soland (1969): An Algorithm for Separable Nonconvex Programming Problems. Management Science 15, pp. 550–569MATHCrossRefMathSciNetGoogle Scholar
  5. Ka/Ro.
    B. Kalantari, J. F. Rosen (1987): An Algorithm for Global Minimization of Linearly Constrained Concave Quadratic Functions. Mathematics of Operations Research 12, pp. 544–561MATHCrossRefMathSciNetGoogle Scholar
  6. Ho/Tu.
    R. Horst, H. Tuy (1996): Global Optimization: Deterministic Approaches. Springer, Berlin, Heidelberg, New YorkMATHGoogle Scholar
  7. Hav.
    C. A. Haverly (1978): Studies of the Behavior of Recursion for the Pooling Problem. ACM SIGMAP Bulletin 25, pp. 19–28CrossRefGoogle Scholar
  8. Fa/Ho.
    J. E. Falk, K. R. Hoffman (1976): A Successive Underestimation Method for Concave Minimization Problems. Math. Oper. Research 1, pp. 251–259Google Scholar
  9. John.
    F. John (1948): Extremum Problems with Inequalities as Subsidiary Conditions. In: Studies and Essays. Courant Anniversary Volume. Interscience, New York, pp. 187–204Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Fak. Mathematik und Wirtschaftswissenschaften Inst. Numerische MathematikUniversität UlmUlmGermany
  2. 2.FB Mathematik und StatistikUniversität KonstanzKonstanzGermany

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