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Super-efficiency of automatic differentiation for functions defined as a minimum
[article]
2020
arXiv
pre-print
In min-min optimization or max-min optimization, one has to compute the gradient of a function defined as a minimum. In most cases, the minimum has no closed-form, and an approximation is obtained via an iterative algorithm. There are two usual ways of estimating the gradient of the function: using either an analytic formula obtained by assuming exactness of the approximation, or automatic differentiation through the algorithm. In this paper, we study the asymptotic error made by these
arXiv:2002.03722v1
fatcat:bumyy323vfexxamteyq3nmez2a