# osqp/osqp

The Operator Splitting QP Solver

Repository: https://github.com/osqp/osqp
Canonical: https://ross.abutalabs.com/products/osqp
Homepage: https://osqp.org
Language: C
License: Apache-2.0
License Family: permissive
Topics: optimization, quadratic-programming, convex-optimization, machine-learning, lasso, svm, control, model-predictive-control, portfolio-optimization, solver, numerical-optimization
Last push: 2026-01-12T12:56:23+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 8, longevity 100
- inputs: {"age_days": 3627, "days_push": 233, "days_rel": 530, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2177, forks 414 (observed 2026-08-28T04:06:22.897093+00:00)

## What it is
OSQP is a C library for solving convex quadratic programs using an ADMM-based operator splitting method, with interfaces to Python, Julia, Matlab, R, Rust, and more. It is library-free, embeddable, warm-startable, and can detect primal/dual infeasibility.

## Use cases
- solve convex quadratic programs with linear constraints
- model predictive control for robotics and control systems
- solve lasso and SVM optimization problems
- portfolio optimization
- embed a QP solver in embedded C code
- solve parametrized QPs efficiently with warm starting

## When to choose
- you need a fast, robust QP solver with no external dependencies
- you need embeddable C code for real-time or embedded control
- your problems are convex QPs and you want warm-starting for repeated solves
- you need infeasibility detection in a first-order QP solver

## When to avoid
- your problem is non-convex or not a quadratic program
- you need extremely high-accuracy solutions where interior-point solvers are better
- you need mixed-integer optimization

## Facets
- artifact type: library
- maturity: stable
- function: math, machine-learning, sdk
- domain: mathematics, machine-learning, robotics, fintech
- platform: cross-platform, python, cpp, rust, jvm, embedded
- tags: quadratic-programming, convex-optimization, admm, model-predictive-control, numerical-optimization, solver, embeddable-c-code, sparse-linear-algebra, algorithms, gpu

## Member repositories
- osqp/osqp (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:22.897093+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:48:50.659214+00:00, confidence not recorded.
  - readme: https://github.com/osqp/osqp (fetched 2026-08-28T04:06:22.897093+00:00, sha a72d2fc2aa69)
  - homepage: https://osqp.org (fetched 2026-08-29T10:28:50.723504+00:00, sha 076a7bee6a64)
  - site_page: https://osqp.org/docs/get_started (fetched 2026-08-29T10:28:50.729872+00:00, sha 9164a96836f7)
  - site_page: https://osqp.org/docs (fetched 2026-08-29T10:28:50.725894+00:00, sha 1a5e1e99b313)
  - site_page: https://osqp.org/docs/release-0.6.3 (fetched 2026-08-29T10:28:50.728010+00:00, sha 712c53d53b7a)
- Data as of 2026-08-30T08:39:29.467469+00:00.