semvec/pypi-packages huggingface dataset:
grast 0.1.2
Lazy automatic differentiation for Python
Requires Python: >=3.10,<4.0

# grast

Lazy automatic differentiation for Python

## Install

Requires python >= 3.10

```sh
pip install grast
```

## Usage

Create function R^n -> R
```py
from grast import var

x = var('x')
y = var('y')
z = var('z').freeze()  # do not compute derivative

h = x/y + y**x
f = z * h + 3
```

Get gradient
```py
df = f.grad()
df_dx = df['x']
df_dy = df['y']
```

Evaluate with specific arguments
```py
args = dict(x=-3, y=5, z=2)
f(args)
df_dx(args)
df_dy(args)
```

View in symbolic format
```py
print(str(f))
print(str(df_dx))
print(str(df_dy))
```

## References

1. 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>

2. 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>