DerivativeNumber class for SuperCollider

Status of this project

This is a first working draft for automatic differentiation in SuperCollider. It follows Jerzy Karczmarczuk 1998, Functional differentiation of computer programs.

See also: - http://conal.net/blog/posts/beautiful-differentiation - http://conal.net/blog/posts/what-is-automatic-differentiation-and-why-does-it-work

It supports partial derivatives, derivatives on floats, arrays, signals, complex numbers.

Some methods of SimpleNumber are missing (like degreeToKey and the boost special functions) and may never be implemented.

There are unit tests for many of the operators.

In the future, there may be a subclass that will do arbitrary order derivatives (see Barak A. Pearlmutter and Jeffrey Mark Siskind 2007 – Lazy Multivariate Higher-Order Forward-Mode AD).

Found no versions tagged via git