Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/35186
Title: | Adjoint differentiation for generic matrix functions |
Author: | Goloubentsev, Andrei Goloubentsev, Dmitri Lakshtanov, Evgeny |
Issue Date: | 13-Oct-2022 |
Publisher: | Infopro Digital Services |
Abstract: | We derive a formula for the adjoint Ā of a square-matrix operation of the form f(A), where f is holomorphic in the neighborhood of each eigenvalue.We consider special cases such as the spectral decomposition A = UDU-1 and the spectrum cutoff f(A) = A+ for symmetric A. We then apply the formula to derive closed-form expressions in particular cases of interest to quantitative finance such as the “nearest correlation matrix” routine and regularized linear regression. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/35186 |
DOI: | 10.21314/JCF.2022.024 |
ISSN: | 1460-1559 |
Publisher Version: | https://www.risk.net/journal-of-computational-finance/7954858/adjoint-differentiation-for-generic-matrix-functions |
Appears in Collections: | CIDMA - Artigos OGTCG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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2109.04913.pdf | 108.3 kB | Adobe PDF | View/Open |
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