torchimize contains implementations of the Gradient Descent, Gauss-Newton and Levenberg-Marquardt optimization algorithms using the PyTorch library. The main motivation for this project is to enable convex optimization on GPUs based on the torch.Tensor class, which (as of 2022) is widely used in the deep learning field. This package features the capability to minimize several least-squares optimization problems at each loop iteration in parallel.
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Release files for torchimize 0.0.16
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Source distribution (sdist)
| File | Size | Uploaded | |
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| torchimize-0.0.16.tar.gz | 26.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| torchimize-0.0.16-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 69.1 kB
Release files / torchimize-0.0.16.tar.gz
| Download URL | torchimize-0.0.16.tar.gz |
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| Size | 26.8 kB |
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Release files / torchimize-0.0.16-py3-none-any.whl
| Download URL | torchimize-0.0.16-py3-none-any.whl |
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| Size | 42.4 kB |
| Tags | Python 3 |
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