Jacobians and Gradients for Cartesian Differential Categories
Jean-Simon Pacaud Lemay (Mount Allison University)

TL;DR
This paper introduces linearly closed Cartesian differential categories to provide a coordinate-free way to define Jacobians and gradients, extending the applicability to important examples like smooth functions.
Contribution
It defines linearly closed Cartesian differential categories and shows how they enable a coordinate-free characterization of Jacobians and gradients, including in smooth function categories.
Findings
Linearly closed Cartesian differential categories include many important examples.
Jacobian is defined as the curry of the derivative in these categories.
Gradient is shown to be the curry of the reverse derivative, equal to the transpose of the Jacobian.
Abstract
Cartesian differential categories come equipped with a differential combinator that formalizes the directional derivative from multivariable calculus. Cartesian differential categories provide a categorical semantics of the differential lambda-calculus and have also found applications in causal computation, incremental computation, game theory, differentiable programming, and machine learning. There has recently been a desire to provide a (coordinate-free) characterization of Jacobians and gradients in Cartesian differential categories. One's first attempt might be to consider Cartesian differential categories which are Cartesian closed, such as models of the differential lambda-calculus, and then take the curry of the derivative. Unfortunately, this approach excludes numerous important examples of Cartesian differential categories such as the category of real smooth functions. In this…
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