Optimization Algorithms using Pytorch
Project description
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.
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.
Source Distribution
torchimize-0.0.16.tar.gz
(26.8 kB
view details)
Built Distribution
File details
Details for the file torchimize-0.0.16.tar.gz.
File metadata
- Download URL: torchimize-0.0.16.tar.gz
- Upload date:
- Size: 26.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | de5bf6bfeed024e2688f526293a0bbbdee4f7354fa56f1b6f227c9049f73f40c |
|
| MD5 | 553fc5d1f0bce2b3132a7f11b96eacfd |
|
| BLAKE2b-256 | 85b137233624fde35ebea6203c60e2dd43b9c4f535b0d68c69092d4e756ca0ef |
Provenance
File details
Details for the file torchimize-0.0.16-py3-none-any.whl.
File metadata
- Download URL: torchimize-0.0.16-py3-none-any.whl
- Upload date:
- Size: 42.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.8.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 43233c8a145cfa2487f81e745232a79c0c1662ce586f4249641c7572ef16e059 |
|
| MD5 | 1e0bc98cfc27f2a3ce259c4a28405be7 |
|
| BLAKE2b-256 | 0b4ddf4a153aced3a24c951c36ccc7036fdb8f654f6809d9000ad6a00df2867c |
