lapx
Linear assignment problem solvers, including single and batch solvers.
Decision gist · record as of 2026-08-14
Yes. lapx is actively maintained, has no known vulnerabilities, and offers a stable, performant implementation of linear assignment solvers with multiple output formats and batch support. Install it if you need to solve assignment problems and want more flexibility or batch processing capabilities. The requirement not to install both lap and lapx simultaneously is a minor gotcha but well-documented.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy; import name is lapx; do not install both lap and lapx simultaneously as they provide the same import namespace.
- Medium install friction due to compiled wheels; pre-built binaries cover Python 3.10–3.14 and major platforms (macOS, Linux, Windows, ARM), reducing build overhead.
- Active maintenance with recent releases and no reported vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal attribution requirements.
last release 2026-01-07 (219 days) · last repo commit 2026-03-03 · 44 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 727,850 downloads/mo, #5,214 on PyPI
Alternatives
Verify before relying
pip install lapx
import numpy as np
import lapx
cost_matrix = np.random.rand(100, 150)
total_cost, row_indices, col_indices = lapx.lapjvx(cost_matrix, extend_cost=True, return_cost=True)
assignments = np.column_stack((row_indices, col_indices))- Performance comparison to scipy.optimize.linear_sum_assignment and other LAP solvers under typical workloads.
- Numerical stability guarantees or known edge cases (e.g., degenerate cost matrices, very large or very small values).
- Memory usage characteristics for batch operations on large matrices.
What it is and what it does
lapx is a linear assignment problem solver package that evolved from maintaining an earlier lap package. It implements Jonker-Volgenant algorithms optimized for both dense (LAPJV) and sparse (LAPMOD) cost matrices, solving the problem of finding optimal one-to-one assignments between two sets of items to minimize total cost. The package supports square and rectangular matrices, single-problem and batch processing, and offers multiple output formats to match different downstream use cases.
The core solver functions are based on academic papers and public-domain implementations. lapx provides lapjv, lapmod, lapjvx (SciPy-style output), lapjvxa (direct assignment array output), lapjvc (optimized for square matrices), lapjvs and lapjvsa (sparse variants), and batch versions of each. It depends only on numpy and is available as pre-built wheels for Python 3.7 through 3.14 across macOS, Linux, Windows, and ARM architectures, with optional performance tuning via environment variables during source builds.
Use it for
- Object tracking: match detected objects across video frames by minimizing distance or appearance cost.
- Bipartite graph matching: find optimal pairings between two sets of nodes in applications like job scheduling or resource allocation.
- Data association: assign sensor measurements to tracked targets in multi-object tracking systems.
- Batch processing: solve multiple assignment problems in parallel using batch solver functions.
- Rectangular assignment: handle cases where the number of rows and columns differ, extending costs as needed.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
lapx is actively maintained, has no known vulnerabilities, and offers a stable, performant implementation of linear assignment solvers with multiple output formats and batch support. Install it if you need to solve assignment problems and want more flexibility or batch processing capabilities. The requirement not to install both lap and lapx simultaneously is a minor gotcha but well-documented.
Install
lapx on PyPI
Before you install
Medium install friction due to compiled wheels; pre-built binaries cover Python 3.10–3.14 and major platforms (macOS, Linux, Windows, ARM), reducing build overhead. Active maintenance with recent releases and no reported vulnerabilities.
Requires numpy; import name is lapx; do not install both lap and lapx simultaneously as they provide the same import namespace.
License in practice
MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal attribution requirements.
Quickstart
pip install lapx
import numpy as np
import lapx
cost_matrix = np.random.rand(100, 150)
total_cost, row_indices, col_indices = lapx.lapjvx(cost_matrix, extend_cost=True, return_cost=True)
assignments = np.column_stack((row_indices, col_indices))Verify before relying
- Performance comparison to scipy.optimize.linear_sum_assignment and other LAP solvers under typical workloads.
- Numerical stability guarantees or known edge cases (e.g., degenerate cost matrices, very large or very small values).
- Memory usage characteristics for batch operations on large matrices.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 219 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 727,850 / month, #5,214 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 :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: Libraries |
Evidence: lapx-0.9.4-cp310-cp310-macosx_10_9_x86_64.whl; lapx-0.9.4-cp310-cp310-macosx_11_0_arm64.whl; lapx-0.9.4-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; lapx-0.9.4-cp310-cp310-manylinux_2_24_armv7l.manylinux_2_31_armv7l.whl; lapx-0.9.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; lapx-0.9.4-cp310-cp310-musllinux_1_2_aarch64.whl; lapx-0.9.4-cp310-cp310-musllinux_1_2_armv7l.whl; lapx-0.9.4-cp310-cp310-musllinux_1_2_x86_64.whl; lapx-0.9.4-cp310-cp310-win_amd64.whl; lapx-0.9.4-cp310-cp310-win_arm64.whl; lapx-0.9.4-cp311-cp311-macosx_10_9_x86_64.whl; lapx-0.9.4-cp311-cp311-macosx_11_0_arm64.whl; lapx-0.9.4-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; lapx-0.9.4-cp311-cp311-manylinux_2_24_armv7l.manylinux_2_31_armv7l.whl; lapx-0.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; lapx-0.9.4-cp311-cp311-musllinux_1_2_aarch64.whl; lapx-0.9.4-cp311-cp311-musllinux_1_2_armv7l.whl; lapx-0.9.4-cp311-cp311-musllinux_1_2_x86_64.whl; lapx-0.9.4-cp311-cp311-win_amd64.whl; lapx-0.9.4-cp311-cp311-win_arm64.whl
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See also lap · munkres · nvidia-cusolver · qpsolvers · ortools · PuLP · nvidia-cusolver-cu12 · linear-operator · Mosek · lineax