Conjugate Direction Methods

David G. Luenberger and Yinyu Ye
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David G. Luenberger: Stanford University
Yinyu Ye: Stanford University

Chapter Chapter 9 in Linear and Nonlinear Programming, 2016, pp 263-284 from Springer

Abstract: 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.

Keywords: Conjugate Gradient; Steep Descent; Global Convergence; Conjugate Gradient Method; Conjugate Gradient Algorithm (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-18842-3_9

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