locuslab/qpth
A fast and differentiable QP solver for PyTorch.
This is a tool for developers building optimization models within PyTorch. It helps incorporate quadratic programming (QP) problems directly into neural networks or other differentiable programs. Developers input QP problem parameters (like cost functions and constraints) and get back optimized solutions, allowing for end-to-end learning systems.
785 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning engineer or researcher who needs to embed and solve quadratic programs efficiently within a PyTorch model, maintaining differentiability for backpropagation.
Not ideal if you are looking for a standalone quadratic programming solver without integration into a deep learning framework, or if you are not working with PyTorch.
Stars
785
Forks
112
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 03, 2024
Commits (30d)
0
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