{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Education","url":"https://skillfed.io/packages/category/education"},{"label":"Testing","url":"https://skillfed.io/packages/category/education-testing"}],"enrichment":{"capability":"Solves linear assignment problems using Jonker-Volgenant and related algorithms, supporting single and batch operations on square and rectangular cost matrices.","skillfed_tags":["optimization","matching-algorithm","batch-processing"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"lapx","links":{"html":"https://skillfed.io/packages/lapx","md":"https://skillfed.io/packages/lapx.md","pypi":"https://pypi.org/project/lapx/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-07","license_spdx":null,"license_treatment":"permissive","name":"lapx","python_support":"supports_current","summary":"Linear assignment problem solvers, including single and batch solvers."},"popularity":{"monthly_downloads":727850,"position":5214,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.4"}