cbcbox 2.902

Binary distribution of the CBC MILP solver (COIN-OR Branch and Cut)

cbcbox

cbcbox is a Python wheel distribution of pre-built, self-contained binaries for the CBC MILP solver (COIN-OR Branch and Cut), built from the latest master branch of the COIN-OR repositories.

All dynamic dependencies (OpenBLAS, libgfortran, etc.) are bundled inside the wheel — no system libraries or separate installation steps are needed.

Highlights

• Parallel branch-and-cut — built with --enable-cbc-parallel. Use -threads=N to solve with multiple threads. CBC distributes the branch-and-bound tree across threads, giving significant speedups on multi-core machines for hard MIP instances.

• AMD fill-reducing ordering — SuiteSparse AMD is compiled in, enabling the high-quality UniversityOfFlorida Cholesky factorization for Clp's barrier (interior point) solver. Compared to the built-in native Cholesky, AMD reordering produces much less fill-in on large sparse problems, making barrier substantially faster. Activate it with -barrier -cholesky UniversityOfFlorida (see barrier usage below).

• Optimised BLAS — linked against OpenBLAS for fast dense linear algebra.

Supported platforms

Platform	Wheel tag
Linux x86_64	manylinux2014_x86_64
Linux aarch64	manylinux2014_aarch64
macOS arm64 (Apple Silicon)	macosx_11_0_arm64
macOS x86_64	macosx_10_9_x86_64
Windows AMD64	win_amd64

Installation

Note: cbcbox is now available on PyPI — pip install cbcbox. Pre-built wheel artifacts are also available from the CI runs (see below).

Installing from a pre-built wheel (recommended)

1. Go to the Actions tab of this repository.

2. Open the latest successful workflow run.

3. Download the artifact matching your platform:

   Artifact name	Platform
   cibw-wheels-Linux-X64	Linux x86_64
   cibw-wheels-Linux-ARM64	Linux aarch64
   cibw-wheels-macOS-ARM64	macOS Apple Silicon
   cibw-wheels-macOS-X64	macOS x86_64
   cibw-wheels-Windows-X64	Windows AMD64

4. Unzip the artifact and install the .whl file:

   pip install cbcbox-*.whl
   

Installing from PyPI

pip install cbcbox

Usage

Command line

Invoke the CBC solver directly via the Python module entry point:

python -m cbcbox mymodel.lp -solve -quit
python -m cbcbox mymodel.mps.gz -solve -quit
python -m cbcbox mymodel.mps -seconds 60 -timem elapsed -solve -quit
python -m cbcbox mymodel.mps -dualp pesteep -solve -quit

CBC accepts LP, MPS and compressed MPS (.mps.gz) files. Pass -help for the full list of options, or -quit to exit after solving.

Parallel branch-and-cut

This build includes parallel branch-and-cut (--enable-cbc-parallel). Use -threads=N to distribute the search tree across N threads:

python -m cbcbox mymodel.mps -threads=4 -timem elapsed -solve -quit

Use -timem elapsed when running parallel so that time limits and reported times reflect wall-clock seconds rather than CPU-time (which would be ~N× the wall time).

Barrier (interior-point) solver

Clp's barrier solver can be faster than simplex for large LP relaxations. This build includes SuiteSparse AMD, which enables the high-quality UniversityOfFlorida Cholesky factorization — significantly reducing fill-in compared to the built-in native Cholesky:

# Solve LP relaxation with barrier + AMD Cholesky, then crossover to simplex basis
python -m cbcbox mymodel.mps -barrier -cholesky UniversityOfFlorida -solve -quit

# Useful as a root-node strategy inside MIP (let CBC use simplex for B&B):
python -m cbcbox mymodel.mps -barrier -cholesky UniversityOfFlorida -solve -quit

Without AMD, only -cholesky native (less efficient) is available.

Python API

The package exposes helpers to locate the installed files:

import cbcbox
import subprocess

# Path to the cbc binary (cbc.exe on Windows).
cbcbox.cbc_bin_path()
# e.g. '/home/user/.venv/lib/python3.13/site-packages/cbcbox/cbc_dist/bin/cbc'

# Directory containing the static and dynamic libraries.
cbcbox.cbc_lib_dir()
# e.g. '.../cbcbox/cbc_dist/lib'

# Directory containing the COIN-OR C/C++ headers.
cbcbox.cbc_include_dir()
# e.g. '.../cbcbox/cbc_dist/include/coin'

# Run CBC programmatically.
result = subprocess.run(
    [cbcbox.cbc_bin_path(), "mymodel.mps", "-solve", "-quit"],
    capture_output=True, text=True,
)
print(result.stdout)

What is built

The build pipeline compiles all components from source inside the CI runner, in the following order:

Component	Version / branch	Purpose
Cbc	master	Branch-and-cut MIP solver
Cgl	master	Cut generation library
Clp	master	Simplex LP solver (used as the MIP node relaxation)
Osi	master	Open Solver Interface
CoinUtils	master	Utility library (shared by all COIN-OR packages)
Nauty	2.8.9	Symmetry detection for MIP presolve
AMD (SuiteSparse v7.12.2)	v7.12.2	Sparse matrix fill-reducing ordering
OpenBLAS	v0.3.31	Optimised BLAS/LAPACK for LP basis factorisation

All COIN-OR components are linked into both static (.a) and shared (.so / .dylib) libraries on Linux and macOS. On Windows only shared libraries (.dll) are produced — MinGW's autotools does not support building static and DLL simultaneously. The shared libraries are patched with self-relative RPATHs and bundled inside the wheel, making them directly usable via cffi or ctypes without any system installation.

Wheel contents

The wheel installs under cbcbox/cbc_dist/ inside the site-packages directory. The layout is:

cbc_dist/
├── bin/
│   ├── cbc           # CBC MIP solver binary  (cbc.exe on Windows)
│   └── clp           # Clp LP solver binary   (clp.exe on Windows)
├── lib/
│   ├── libCbc.a / libCbc.so            # CBC solver
│   ├── libCbcSolver.a / libCbcSolver.so
│   ├── libClp.a / libClp.so            # Clp LP solver
│   ├── libCgl.a / libCgl.so            # Cut generation
│   ├── libOsi.a / libOsi.so            # Solver interface
│   ├── libOsiClp.a / libOsiClp.so      # Clp OSI binding
│   ├── libOsiCbc.a / libOsiCbc.so      # CBC OSI binding (where available)
│   ├── libCoinUtils.a / libCoinUtils.so
│   ├── libamd.a                        # AMD sparse ordering (static only)
│   ├── libsuitesparseconfig.a          # SuiteSparse config (static only)
│   ├── libnauty.a                      # Nauty (static only)
│   ├── libopenblas.a / libopenblas.so  # OpenBLAS BLAS/LAPACK
│   ├── pkgconfig/                      # .pc files for all libraries
│   └── <bundled runtime shared libs>   # Platform-specific — see below
└── include/
    ├── coin/      # COIN-OR headers (CoinUtils, Osi, Clp, Cgl, Cbc)
    ├── nauty/     # Nauty headers
    └── *.h        # SuiteSparse / AMD headers

Bundled dynamic libraries

Because the static COIN-OR libraries link to OpenBLAS, which in turn links to the Fortran runtime, the following shared libraries are bundled inside the wheel and their paths are rewritten so no system installation is required.

Linux (lib/ directory, RPATH set to $ORIGIN)

Library	Description
libopenblas.so.0	OpenBLAS BLAS/LAPACK
libgfortran.so.5	GNU Fortran runtime
libquadmath.so.0	Quad-precision math (dependency of libgfortran)

macOS (lib/ directory, install names rewritten to @rpath/)

Library	Description
libopenblas.dylib	OpenBLAS BLAS/LAPACK
libgfortran.5.dylib	GNU Fortran runtime
libgcc_s.1.1.dylib	GCC runtime
libquadmath.0.dylib	Quad-precision math

Windows (bin/ directory, DLLs placed next to the executable)

Library	Description
libopenblas.dll	OpenBLAS BLAS/LAPACK
libgfortran-5.dll	GNU Fortran runtime
libgcc_s_seh-1.dll	GCC SEH runtime
libquadmath-0.dll	Quad-precision math
libstdc++-6.dll	C++ standard library (MinGW64)
libwinpthread-1.dll	POSIX thread emulation

CI / build pipeline

Wheels are built and tested automatically via GitHub Actions using cibuildwheel. The workflow (.github/workflows/wheel.yml) runs on five separate runners:

Runner	Produces
ubuntu-latest	manylinux2014_x86_64 wheel
ubuntu-24.04-arm	manylinux2014_aarch64 wheel
macos-15	macosx_11_0_arm64 wheel
macos-15-intel	macosx_10_9_x86_64 wheel
windows-latest	win_amd64 wheel

After each wheel is built, the test suite in tests/ is run against the installed wheel to verify correctness.

Integration tests

The test suite (pytest) solves two MIP instances and checks the optimal objective values, both in single-threaded and parallel (2-thread) modes:

Test	Instance	Expected optimal
test_solve[pp08a]	pp08a.mps.gz (240×182)	7350
test_solve[sprint_hidden06_j]	sprint_hidden06_j.mps.gz (3694×10210)	130
test_solve_parallel[pp08a]	same, -threads=3	7350
test_solve_parallel[sprint_hidden06_j]	same, -threads=3	130

The parallel tests verify that --enable-cbc-parallel is functional.

Publishing to PyPI

Note: cbcbox is not yet registered on PyPI. When ready, trigger the workflow manually and select pypi (or testpypi) in the Publish input. Trusted Publisher (OIDC) authentication is used — no API tokens are stored as secrets.

License

CBC and all COIN-OR components are distributed under the Eclipse Public License 2.0. OpenBLAS is distributed under the BSD 3-Clause licence. SuiteSparse AMD is distributed under the BSD 3-Clause licence. Nauty is distributed under the Apache 2.0 licence.