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Original language | English |
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Pages (from-to) | 988-994 |
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Number of pages | 7 |
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Journal | Optimization Methods and Software |
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Volume | 33 |
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Issue | 4-6 |
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Early online date | 26 Jan 2018 |
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DOIs | |
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Publication status | Published - 2 Nov 2018 |
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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