highspy
A thin set of pybind11 wrappers to HiGHS
Decision gist · record as of 2026-08-14
Yes. Highspy is actively maintained, widely used (top 5000 PyPI packages), has no known vulnerabilities, and offers a permissive MIT license. Medium install friction is typical for compiled solvers. Install it if you need to solve LP, QP, or MIP problems in Python and want a self-contained, dependency-light solver.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Medium install friction due to compiled wheels for multiple platforms and Python versions (3.9–3.14).
- Active maintenance with recent releases; last commit 2026-08-14.
- Depends on numpy and typing_extensions, both widely available.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you may use, modify, and distribute highspy and derivative works freely with minimal restrictions.
last release 2026-07-02 (43 days) · last repo commit 2026-08-14 · 1,786 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,622,175 downloads/mo, #2,554 on PyPI
Alternatives
Verify before relying
pip install highspy
import highspy
h = highspy.Highs()
# Define and solve an optimization model- Whether the solver supports warm-starting from previous solutions or basis information
- Performance characteristics and scalability limits for large-scale problems
- Availability of detailed API documentation beyond the GitHub repository
What it is and what it does
Highspy provides a thin Python interface to HiGHS, a C++ optimization solver that handles linear, quadratic, and mixed-integer programming problems. It wraps the core solver functionality via pybind11, allowing Python developers to formulate and solve optimization models without writing C++ code. The package depends on numpy for numerical operations and typing_extensions for type hints.
The solver implements primal and dual revised simplex algorithms for LP, an interior-point method for LP, an active-set method for QP, and a branch-and-bound approach for MIP. It is designed for large-scale sparse problems and supports both serial and parallel execution. No third-party solver dependencies are required—HiGHS is self-contained.
Use it for
- Solve linear programming problems such as resource allocation, production planning, or network flow optimization
- Handle mixed-integer programming for discrete optimization tasks like scheduling or facility location
- Solve convex quadratic programming problems in portfolio optimization or machine learning applications
- Integrate optimization into Python data science workflows alongside numpy and pandas
- Prototype optimization models quickly without switching to specialized modeling languages
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Highspy is actively maintained, widely used (top 5000 PyPI packages), has no known vulnerabilities, and offers a permissive MIT license. Medium install friction is typical for compiled solvers. Install it if you need to solve LP, QP, or MIP problems in Python and want a self-contained, dependency-light solver.
Install
highspy on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms and Python versions (3.9–3.14). Active maintenance with recent releases; last commit 2026-08-14. Depends on numpy and typing_extensions, both widely available.
License in practice
MIT license is permissive; you may use, modify, and distribute highspy and derivative works freely with minimal restrictions.
Quickstart
pip install highspy
import highspy
h = highspy.Highs()
# Define and solve an optimization modelVerify before relying
- Whether the solver supports warm-starting from previous solutions or basis information
- Performance characteristics and scalability limits for large-scale problems
- Availability of detailed API documentation beyond the GitHub repository
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesnumpytyping_extensions |
| Maintenance | Actively maintained 43 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,622,175 / month, #2,554 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Typing :: Typed |
Evidence: highspy-1.15.1-cp310-cp310-macosx_10_9_x86_64.whl; highspy-1.15.1-cp310-cp310-macosx_11_0_arm64.whl; highspy-1.15.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; highspy-1.15.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; highspy-1.15.1-cp310-cp310-manylinux_2_26_i686.manylinux_2_28_i686.whl; highspy-1.15.1-cp310-cp310-musllinux_1_2_aarch64.whl; highspy-1.15.1-cp310-cp310-musllinux_1_2_i686.whl; highspy-1.15.1-cp310-cp310-musllinux_1_2_x86_64.whl; highspy-1.15.1-cp310-cp310-win32.whl; highspy-1.15.1-cp310-cp310-win_amd64.whl; highspy-1.15.1-cp311-cp311-macosx_10_9_x86_64.whl; highspy-1.15.1-cp311-cp311-macosx_11_0_arm64.whl; highspy-1.15.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; highspy-1.15.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; highspy-1.15.1-cp311-cp311-manylinux_2_26_i686.manylinux_2_28_i686.whl; highspy-1.15.1-cp311-cp311-musllinux_1_2_aarch64.whl; highspy-1.15.1-cp311-cp311-musllinux_1_2_i686.whl; highspy-1.15.1-cp311-cp311-musllinux_1_2_x86_64.whl; highspy-1.15.1-cp311-cp311-win32.whl; highspy-1.15.1-cp311-cp311-win_amd64.whl
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