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Conjugate Direction Methods

In: Linear and Nonlinear Programming

Author

Listed:
  • David G. Luenberger

    (Stanford University)

  • Yinyu Ye

    (Stanford University)

Abstract

Conjugate direction methods can be regarded as being somewhat intermediate between the method of steepest descent and Newton’s method. They are motivated by the desire to accelerate the typically slow convergence associated with steepest descent while avoiding the information requirements associated with the evaluation, storage, and inversion of the Hessian (or at least solution of a corresponding system of equations) as required by Newton’s method.

Suggested Citation

  • David G. Luenberger & Yinyu Ye, 2016. "Conjugate Direction Methods," International Series in Operations Research & Management Science, in: Linear and Nonlinear Programming, edition 4, chapter 0, pages 263-284, Springer.
  • Handle: RePEc:spr:isochp:978-3-319-18842-3_9
    DOI: 10.1007/978-3-319-18842-3_9
    as

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