-
opt_einsum — opt_einsum v3.3.0 documentation
\[M_{pqrs} = C_{pi} C_{qj} I_{ijkl} C_{rk} C_{sl}\]
and consider two different algorithms:
import numpy as np
dim = 10
I = np.random.rand(dim, dim, dim, dim)
C = np.random.rand(dim, dim)
def naive(I...
-
Welcome to Read the Docs — Optimized Einsum latest documentation
/docs) directory in your repository.
If you want to use another markup, choose a different builder in your settings.
Check out our Getting Started Guide to become more
familiar with Read the Docs.
...
-
Introduction — opt_einsum v3.3.0 documentation
The
The
The
By default (
optimize='auto'),
contract() will select the
best of these it can while aiming to keep path finding times below around 1ms.
An analysis of each of these approaches’ perfor...
-
Install opt_einsum — opt_einsum v3.3.0 documentation
conda install opt_einsum -c conda-forge
This installs opt_einsum and the NumPy dependancy.
The opt_einsum package is maintained on the
conda-forge channel.
To install opt_einsum with
pip there are a...
-
Input Format — opt_einsum v3.3.0 documentation
anything hashable and comparable such as
str in the subscript list.
A simple example of this syntax is:
>>> x, y, z = np.ones((1, 2)), np.ones((2, 2)), np.ones((2, 1))
>>> oe.contract(x, ('left', 'bo...
-
Function Reference — opt_einsum v3.3.0 documentation
optimal approach, but with extra heuristic early pruning of branches as well sieving by
memory_limit and the best path found so far.