nonconvexAG
This is a GPU implementation of restarting accelerated gradient algorithm with strong rules for (high-dimensional) nonconvex sparse learning problems. The corresponding paper can be found at arXiv.
Metadata
Release files for nonconvexAG-GPU 1.0.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nonconvexAG_GPU-1.0.8.tar.gz | 43.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nonconvexAG_GPU-1.0.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 74.0 kB
Release files / nonconvexAG_GPU-1.0.8.tar.gz
| Download URL | nonconvexAG_GPU-1.0.8.tar.gz |
|---|---|
| Size | 43.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
8675b2631d6bb08c2b0fed1eae0cce4e65acf1d7a3662ebbd9d9e7aa95723a29
|
|
BLAKE2b-256 checksum How to use checksums |
c85b7f986b7718c6b55bd8a355d2c7fac6299fb281cd7c5122180edf8c1e4d6b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.15
|
Release files / nonconvexAG_GPU-1.0.8-py3-none-any.whl
| Download URL | nonconvexAG_GPU-1.0.8-py3-none-any.whl |
|---|---|
| Size | 31.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
23b617fb0e7f2cd4d1ce06046de6abb23a34c34a55ed3176fa9154c9d2d5ae36
|
|
BLAKE2b-256 checksum How to use checksums |
99e6f929c3bb19255b1600970077b1452a7173c91acfa992767cb73ff5f91ea7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.15
|