https://github.com/osqp/osqp

The Operator Splitting QP Solver
https://github.com/osqp/osqp

control convex-optimization lasso machine-learning model-predictive-control numerical-optimization optimization portfolio-optimization quadratic-programming solver svm

Last synced: about 2 months ago
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The Operator Splitting QP Solver

• Host: GitHub
• URL: https://github.com/osqp/osqp
• Owner: osqp
• License: apache-2.0
• Created: 2016-09-27T03:02:46.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2025-04-09T21:47:55.000Z (3 months ago)
• Last Synced: 2025-04-12T12:21:40.554Z (3 months ago)
• Topics: control, convex-optimization, lasso, machine-learning, model-predictive-control, numerical-optimization, optimization, portfolio-optimization, quadratic-programming, solver, svm
• Language: C
• Homepage: https://osqp.org
• Size: 57.4 MB
• Stars: 1,837
• Watchers: 41
• Forks: 374
• Open Issues: 98
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: docs/contributing/index.rst
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# The Operator Splitting QP Solver

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[**Visit our GitHub Discussions page**](https://github.com/orgs/osqp/discussions) for any questions related to the solver!

**The documentation** is available at [**osqp.org**](https://osqp.org/)

The OSQP (Operator Splitting Quadratic Program) solver is a numerical optimization package for solving problems in the form

```

minimize        0.5 x' P x + q' x

subject to      l <= A x <= u

```

where `x in R^n` is the optimization variable. The objective function is defined by a positive semidefinite matrix `P in S^n_+` and vector `q in R^n`. The linear constraints are defined by matrix `A in R^{m x n}` and vectors `l` and `u` so that `l_i in R U {-inf}` and `u_i in R U {+inf}` for all `i in 1,...,m`.

## Citing OSQP

If you are using OSQP for your work, we encourage you to

* [Cite the related papers](https://osqp.org/citing/),

* Put a star on this repository.

**We are looking forward to hearing your success stories with OSQP!** Please [share them with us](mailto:[email protected]).

## Bug reports and support

Please report any issues via the [Github issue tracker](https://github.com/osqp/osqp/issues). All types of issues are welcome including bug reports, documentation typos, feature requests and so on.

## Numerical benchmarks

Numerical benchmarks against other solvers are available [here](https://github.com/osqp/osqp_benchmarks).