University of Hertfordshire

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Differentiating through Conjugate Gradient

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Original languageEnglish
Number of pages7
Pages (from-to)988-994
JournalOptimization Methods and Software
Journal publication date2 Nov 2018
Volume33
Issue4-6
Early online date26 Jan 2018
DOIs
Publication statusPublished - 2 Nov 2018

Abstract

We show that, although the Conjugate Gradient (CG) Algorithm has a singularity at the solution, it is possible to differentiate forward through the algorithm automatically by re-declaring all the variables as truncated Taylor series, the type of active variable widely used in Automatic Differentiation (AD) tools such as ADOL-C. If exact arithmetic is used, this approach gives a complete sequence of correct directional derivatives of the solution, to arbitrary order, in a single cycle of at most n iterations, where n is the number of dimensions. In the inexact case the approach emphasizes the need for a means by which the programmer can communicate certain conditions involving derivative values directly to an AD tool.

Notes

This is the pre-print version of an article published by Taylor & Francis in Optimization Methods and Software on 6 January 2018, available online at: https://doi.org/10.1080/10556788.2018.1425862.

ID: 12798171