Automatic Differentiation With Higher Infinitesimals, or Computational Smooth Infinitesimal Analysis in Weil Algebra

06/27/2021
by   Hiromi Ishii, et al.
0

We propose an algorithm to compute the C^∞-ring structure of arbitrary Weil algebra. It allows us to do some analysis with higher infinitesimals numerically and symbolically. To that end, we first give a brief description of the (Forward-mode) automatic differentiation (AD) in terms of C^∞-rings. The notion of a C^∞-ring was introduced by Lawvere and used as the fundamental building block of smooth infinitesimal analysis and synthetic differential geometry. We argue that interpreting AD in terms of C^∞-rings gives us a unifying theoretical framework and modular ways to express multivariate partial derivatives. In particular, we can "package" higher-order Forward-mode AD as a Weil algebra, and take tensor products to compose them to achieve multivariate higher-order AD. The algorithms in the present paper can also be used for a pedagogical purpose in learning and studying smooth infinitesimal analysis as well.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/17/2021

Higher Order Automatic Differentiation of Higher Order Functions

We present semantic correctness proofs of automatic differentiation (AD)...
research
03/22/2021

A Succinct Multivariate Lazy Multivariate Tower AD for Weil Algebra Computation

We propose a functional implementation of Multivariate Tower Automatic D...
research
07/26/2016

Forward-Mode Automatic Differentiation in Julia

We present ForwardDiff, a Julia package for forward-mode automatic diffe...
research
01/07/2020

Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing

We present semantic correctness proofs of Automatic Differentiation (AD)...
research
12/21/2022

Forward- or Reverse-Mode Automatic Differentiation: What's the Difference?

Automatic differentiation (AD) has been a topic of interest for research...
research
10/26/2018

Automatic differentiation in ML: Where we are and where we should be going

We review the current state of automatic differentiation (AD) for array ...
research
01/20/2021

MultivariateApart: Generalized Partial Fractions

We present a package to perform partial fraction decompositions of multi...

Please sign up or login with your details

Forgot password? Click here to reset