{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Provides a unified Python interface to solve convex quadratic programs using a choice of backend solvers, returning either the primal solution or primal and dual results.","skillfed_tags":["optimization","numerical-computing","solver-abstraction"],"use_cases":["Solve constrained least-squares problems by converting them to QP form with appropriate P and q matrices.","Implement model predictive control or trajectory optimization in robotics by solving QPs at each time step.","Benchmark multiple QP solvers on the same problem to find the fastest or most accurate for your use case.","Add penalty terms (ridge regression, LASSO-like regularization) by incorporating them into the QP objective.","Extract dual multipliers and sensitivity information for post-solution analysis or warm-starting."],"what_it_does":"qpsolvers is a wrapper library that provides a single `solve_qp` function to solve convex quadratic programs with multiple backend solvers. It abstracts away the differences between solvers like OSQP, ProxQP, Clarabel, and others, allowing you to write solver-agnostic code and switch backends by changing a keyword argument. The library accepts problem matrices as NumPy arrays (for dense solvers) or SciPy CSC sparse matrices (for sparse solvers), and returns the optimal solution vector or None if the problem is infeasible.\n\nThe package also offers a `solve_problem` function that returns not just the primal solution but also dual multipliers and other solver-computed quantities. It supports standard QP form with linear inequality constraints (Gx \u2264 h), equality constraints (Ax = b), and variable bounds (lb \u2264 x \u2264 ub). The library automatically detects which solvers are available on your system and lists them in `qpsolvers.available_solvers`, letting you choose based on your problem structure and performance needs.","worth_installing":"Yes. The package is actively maintained, has low install friction, no known vulnerabilities, and solves a well-defined numerical problem with a clean API. The copyleft license requires attention if you plan to distribute derivative works, but the library itself and its optional backends cover both open-source and commercial use cases. Install it if you need to solve convex quadratic programs or want to experiment with multiple QP solvers."},"id":"qpsolvers","links":{"html":"https://skillfed.io/packages/qpsolvers","md":"https://skillfed.io/packages/qpsolvers.md","pypi":"https://pypi.org/project/qpsolvers/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-19","license_spdx":null,"license_treatment":"copyleft","name":"qpsolvers","python_support":"supports_current","summary":"Quadratic programming solvers in Python with a unified API."},"popularity":{"monthly_downloads":831731,"position":4943,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"4.13.0"}