https://github.com/nrgrp/dlfm
Multi-convex programming for discrete latent factor models prototyping
https://github.com/nrgrp/dlfm
convex-optimization latent-variable-models model-fitting multi-convex-programming
Last synced: 5 months ago
JSON representation
Multi-convex programming for discrete latent factor models prototyping
- Host: GitHub
- URL: https://github.com/nrgrp/dlfm
- Owner: nrgrp
- License: mit
- Created: 2025-02-20T11:39:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-11-10T10:35:24.000Z (8 months ago)
- Last Synced: 2025-11-10T12:19:02.863Z (8 months ago)
- Topics: convex-optimization, latent-variable-models, model-fitting, multi-convex-programming
- Language: Jupyter Notebook
- Homepage:
- Size: 901 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# dlfm
Code accompanies the paper "[Multi-convex Programming for Discrete Latent Factor Models Prototyping](https://haozhu10015.github.io/papers/mcp_dlfm.html)".
The [implementation.ipynb](implementation.ipynb) contains the framework
implementation described in section 5 of the paper.
The jupyter notebook corresponding to the examples mentioned
in the paper are under the folder [examples](examples).
## Dependencies
We manage dependencies through [uv](https://docs.astral.sh/uv/).
Once you have installed uv you can perform
```shell
make install
```
to replicate the virtual environment we have defined in
[pyproject.toml](pyproject.toml) and locked in [uv.lock](uv.lock).
## Run the examples
To reproduce the examples mentioned in the paper, we install [JupyterLab](https://jupyter.org/)
on the fly within the aforementioned virtual environment.
Executing
```shell
make jupyter
```
will install and start the jupyter lab.