lap
Linear Assignment Problem solver (LAPJV/LAPMOD).
Decision gist · record as of 2026-08-14
Yes. lap is a focused, well-maintained solver for a specific algorithmic problem with no known vulnerabilities, permissive licensing, and broad platform coverage. Install it if you need to solve linear assignment problems and prefer a specialized implementation over a general-purpose optimizer. The medium install friction is offset by pre-built wheels and active maintenance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a C++ compiler if building from source; pre-built wheels available for common platforms eliminate this for most users.
- Medium install friction due to compiled C++ components, but pre-built wheels cover modern Python versions (3.7–3.14) across Windows, Linux, and macOS architectures.
- Repository is actively maintained with recent commits.
License · maintenance · safety
BSD-2-Clause (permissive) — Released under BSD-2-Clause (permissive), allowing commercial and private use with minimal restrictions—suitable for most projects.
last release 2026-02-23 (172 days) · last repo commit 2026-02-23 · 252 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,749,785 downloads/mo, #3,594 on PyPI
Alternatives
Verify before relying
pip install lap
import lap
import numpy as np
cost, x, y = lap.lapjv(np.random.rand(4, 5), extend_cost=True)
print(cost, x, y)- Performance comparison with scipy.optimize.linear_sum_assignment or other solvers on typical problem sizes.
- Exact behavior and performance trade-off threshold between LAPJV and LAPMOD for sparse matrices.
What it is and what it does
lap is a specialized solver for the linear assignment problem—the task of finding a minimum-cost perfect matching between two sets of items. It implements two algorithms from academic literature: LAPJV (Jonker-Volgenant) for dense cost matrices and LAPMOD (Volgenant-Mordecai) for sparse ones. The package takes a cost matrix as input and returns the total assignment cost plus two index arrays describing which row is assigned to which column and vice versa.
The solver is built from scratch based on published papers and a public-domain Pascal reference implementation. It returns assignment indices rather than a full assignment matrix, keeping the output compact. The package has wheels for Python 3.7–3.14 across Windows, Linux, and macOS, with support for both numpy 1.x and 2.x, making it straightforward to install on most systems.
Use it for
- Object tracking: match detected objects across video frames by minimizing spatial distance cost.
- Bipartite graph matching: find optimal pairings in workforce scheduling or resource allocation problems.
- Image registration: align keypoints between two images by minimizing coordinate mismatch cost.
- Data association: link sensor measurements to tracked targets in multi-object tracking systems.
- Auction algorithms: solve procurement or assignment auctions where cost represents bid or preference.
- Sequence alignment: match elements between two ordered sequences to minimize dissimilarity.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
lap is a focused, well-maintained solver for a specific algorithmic problem with no known vulnerabilities, permissive licensing, and broad platform coverage. Install it if you need to solve linear assignment problems and prefer a specialized implementation over a general-purpose optimizer. The medium install friction is offset by pre-built wheels and active maintenance.
Install
lap on PyPI
Before you install
Medium install friction due to compiled C++ components, but pre-built wheels cover modern Python versions (3.7–3.14) across Windows, Linux, and macOS architectures. Repository is actively maintained with recent commits.
Requires a C++ compiler if building from source; pre-built wheels available for common platforms eliminate this for most users.
License in practice
Released under BSD-2-Clause (permissive), allowing commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install lap
import lap
import numpy as np
cost, x, y = lap.lapjv(np.random.rand(4, 5), extend_cost=True)
print(cost, x, y)Verify before relying
- Performance comparison with scipy.optimize.linear_sum_assignment or other solvers on typical problem sizes.
- Exact behavior and performance trade-off threshold between LAPJV and LAPMOD for sparse matrices.
Package facts
| License | BSD-2-Clause permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 172 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,749,785 / month, #3,594 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: EducationTopic :: Education :: TestingTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: MathematicsTopic :: Software Development |
Evidence: lap-0.5.13-cp310-cp310-macosx_10_9_x86_64.whl; lap-0.5.13-cp310-cp310-macosx_11_0_arm64.whl; lap-0.5.13-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl; lap-0.5.13-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; lap-0.5.13-cp310-cp310-musllinux_1_2_aarch64.whl; lap-0.5.13-cp310-cp310-musllinux_1_2_x86_64.whl; lap-0.5.13-cp310-cp310-win_amd64.whl; lap-0.5.13-cp310-cp310-win_arm64.whl; lap-0.5.13-cp311-cp311-macosx_10_9_x86_64.whl; lap-0.5.13-cp311-cp311-macosx_11_0_arm64.whl; lap-0.5.13-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl; lap-0.5.13-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; lap-0.5.13-cp311-cp311-musllinux_1_2_aarch64.whl; lap-0.5.13-cp311-cp311-musllinux_1_2_x86_64.whl; lap-0.5.13-cp311-cp311-win_amd64.whl; lap-0.5.13-cp311-cp311-win_arm64.whl; lap-0.5.13-cp312-cp312-macosx_10_13_x86_64.whl; lap-0.5.13-cp312-cp312-macosx_11_0_arm64.whl; lap-0.5.13-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl; lap-0.5.13-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
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See also lapx · munkres · nvidia-cusolver · qdldl · tabmat · jenkspy · quadprog · nvidia-cusolver-cu12 · qpsolvers · leidenalg