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- W1835967569 abstract "We derive algorithms for higher order derivative computation of the rectangular $QR$ and eigenvalue decomposition of symmetric matrices with distinct eigenvalues in the forward and reverse mode of algorithmic differentiation (AD) using univariate Taylor propagation of matrices (UTPM). Linear algebra functions are regarded as elementary functions and not as algorithms. The presented algorithms are implemented in the BSD licensed AD tool texttt{ALGOPY}. Numerical tests show that the UTPM algorithms derived in this paper produce results close to machine precision accuracy. The theory developed in this paper is applied to compute the gradient of an objective function motivated from optimum experimental design: $nabla_x Phi(C(J(F(x,y))))$, where $Phi = {lambda_1 : lambda_1 C}$, $C = (J^T J)^{-1}$, $J = frac{dd F}{dd y}$ and $F = F(x,y)$." @default.
- W1835967569 created "2016-06-24" @default.
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- W1835967569 date "2010-01-11" @default.
- W1835967569 modified "2023-09-27" @default.
- W1835967569 title "Algorithmic Differentiation of Linear Algebra Functions with Application in Optimum Experimental Design (Extended Version)" @default.
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- W1835967569 doi "https://doi.org/10.48550/arxiv.1001.1654" @default.
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