Consistent treatment of incompletely converged iterative linear solvers in reverse-mode algorithmic differentiation

Siamak Akbarzadeh (), Jan Hückelheim and Jens-Dominik Müller ()
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Siamak Akbarzadeh: Queen Mary University of London (SEMS)
Jan Hückelheim: Queen Mary University of London (SEMS)
Jens-Dominik Müller: Queen Mary University of London (SEMS)

Computational Optimization and Applications, 2020, vol. 77, issue 2, No 11, 597-616

Abstract: Abstract Algorithmic differentiation (AD) is a widely-used approach to compute derivatives of numerical models. Many numerical models include an iterative process to solve non-linear systems of equations. To improve efficiency and numerical stability, AD is typically not applied to the linear solvers. Instead, the differentiated linear solver call is replaced with hand-produced derivative code that exploits the linearity of the original call. In practice, the iterative linear solvers are often stopped prematurely to recompute the linearisation of the non-linear outer loop. We show that in the reverse-mode of AD, the derivatives obtained with partial convergence become inconsistent with the original and the tangent-linear models, resulting in inaccurate adjoints. We present a correction term that restores consistency between adjoint and tangent-linear gradients if linear systems are only partially converged. We prove the consistency of this correction term and show in numerical experiments that the accuracy of adjoint gradients of an incompressible flow solver applied to an industrial test case is restored when the correction term is used.

Keywords: Algorithmic differentiation; Reverse-mode; Iterative linear solvers; Differentiated solver replacement (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10589-020-00214-x

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