Composing Automatic Differentiation with Custom Derivatives of Higher-Order Functions

Fuente: arXiv
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Autore principale: Estep, Sam
Natura: Preprint
Pubblicazione: 2024
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author Estep, Sam
author_facet Estep, Sam
contents Recent theoretical work on automatic differentiation (autodiff) has focused on characteristics such as correctness and efficiency while assuming that all derivatives are automatically generated by autodiff using program transformation, with the exception of a fixed set of derivatives for primitive operations. However, in practice this assumption is insufficient: the programmer often needs to provide custom derivatives for composite functions to achieve efficiency and numerical stability. In this work, we start from the untyped lambda calculus with a reverse-mode autodiff operator, extend it with an operator to attach manual derivatives, and demonstrate its utility via several examples.
format Preprint
id arxiv_https___arxiv_org_abs_2408_07683
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Composing Automatic Differentiation with Custom Derivatives of Higher-Order Functions
Estep, Sam
Programming Languages
Recent theoretical work on automatic differentiation (autodiff) has focused on characteristics such as correctness and efficiency while assuming that all derivatives are automatically generated by autodiff using program transformation, with the exception of a fixed set of derivatives for primitive operations. However, in practice this assumption is insufficient: the programmer often needs to provide custom derivatives for composite functions to achieve efficiency and numerical stability. In this work, we start from the untyped lambda calculus with a reverse-mode autodiff operator, extend it with an operator to attach manual derivatives, and demonstrate its utility via several examples.
title Composing Automatic Differentiation with Custom Derivatives of Higher-Order Functions
topic Programming Languages
url https://arxiv.org/abs/2408.07683