pyqpmad 1.4.0

Python wrapper for qpmad

pyqpmad

A fast Python wrapper for qpmad, a C++ Quadratic Programming (QP) solver based on Goldfarb-Idnani's active-set method. pyqpmad is built using nanobind for high-performance Python bindings and Eigen for efficient linear algebra.

Features

• Solves Unconstrained, Constrained, and Bounded Quadratic Programs.
• Lightweight and fast wrapper utilizing nanobind.
• Automatic compilation of dependencies (qpmad, eigen, nanobind) from source via CMake.

Installation

Using uv (Recommended)

You can easily build and install this package using uv, an extremely fast Python package installer and resolver.

To install it directly:

uv pip install .

To run the tests:

uv run --with pytest  test_qpmad.py

Using pip

pip install .

To run tests with pip:

pip install pytest
pytest test_qpmad.py

Build Explanation

The build process is managed entirely by CMake. When you install the package (via pip or uv), CMake's FetchContent module is triggered to automatically download the required dependencies:

• nanobind (v2.12.0)
• eigen (v5.0.1 mirror)
• qpmad (v1.4.0)

CMake then configures qpmad as an interface library linked with Eigen, and compiles the Python wrapper (qpmad_pywrap.cpp) using nanobind. Finally, Python type stubs (.pyi) are generated for IDE type hinting.

Example Usage

Here is a quick example demonstrating how to solve a constrained QP problem.

Problem: Minimize $x^2 + y^2$ subject to $x + y = 1$

import pyqpmad
import numpy as np

solver = pyqpmad.Solver()

# Define the Hessian matrix (H) and linear term (h)
H = np.array([[2.0, 0.0], 
              [0.0, 2.0]], order='F')
h = np.array([0.0, 0.0])

# Define the linear constraint: 1.0 <= x + y <= 1.0
A = np.array([[1.0, 1.0]], order='F')
Alb = np.array([1.0]) # Lower bound
Aub = np.array([1.0]) # Upper bound

# Pre-allocate output array
primal = np.zeros(2)

# Solve
status = solver.solve(primal, H, h, A=A, Alb=Alb, Aub=Aub)

if status == pyqpmad.ReturnStatus.OK:
    print(f"Optimal solution found: {primal}") # Output: [0.5 0.5]
else:
    print(f"Failed to solve. Status: {status}")

For more examples—such as solving unconstrained problems or adding lower/upper bounds—please check test_qpmad.py.

Release Pipeline

This project uses cibuildwheel integrated via GitHub Actions to automatically build wheels for Linux, macOS, and Windows.

Triggering a Release

To publish a new release to PyPI, tag the main branch with a version number starting with v (e.g., v1.0.0) and push the tag to GitHub:

git tag v1.0.0
git push origin v1.0.0

The GitHub Actions workflow will:

1. Build sdist and wheels for all major platforms.
2. Run tests on the generated wheels.
3. Automatically publish the artifacts to PyPI.