No project description provided
Project description
grast
Automatic differentiation of generic fields for Python
Install
pip install grast
Usage
Create function R^n -> R
from grast import var
x = var('x')
y = var('y')
f = x/y + y**x
Get gradient
df = f.grad()
df_dx = df['x']
df_dy = df['y']
Evaluate with specific arguments
args = dict(x=-3, y=5)
f(args)
df_dx(args)
df_dy(args)
View in symbolic format
print(str(f))
print(str(df_dx))
print(str(df_dy))
References
-
F. Krawiec, S. Peyton Jones, N. Krishnaswami, T. Ellis, R. A. Eisenberg, A. Fitzgibbon. 2022. Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation. Proc. ACM Program. Lang., 6, POPL (2022), 1–30. https://doi.org/10.1145/3498710
-
Jerzy Karczmarczuk. 1998. Functional Differentiation of Computer Programs. In Proceedings of the Third ACM SIGPLAN International Conference on Functional Programming (Baltimore, Maryland, USA) (ICFP ’98). Association for Computing Machinery, New York, NY, USA, 195-203. https://doi.org/10.1145/289423.289442
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
