The method of shortest residuals is briefly discussed in Chap. 1. We show there that the method differs from a standard conjugate gradient algorithm only by scaling factors applied to conjugate directions. This is true when problems with quadratics are considered. However, these methods are quite different if applied to nonconvex functions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2009). The Method of Shortest Residuals and Nondifferentiable Optimization. In: Conjugate Gradient Algorithms in Nonconvex Optimization. Nonconvex Optimization and Its Applications, vol 89. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85634-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-85634-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85633-7
Online ISBN: 978-3-540-85634-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)