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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...
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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. ...
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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...
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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...
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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...
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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.