@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md

OSQP - The Operator Splitting QP Solver

GitHub release versionLicense

PyPI - downloadsConda - downloadsJulia - downloads

OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM). It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications. Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from modelling languages such as CVXPY, JuMP, and YALMIP.

Visit the documentation to learn how to use OSQP.

Visit our GitHub Discussions page for any questions related to the solver!

If you use OSQP in an academic work, please cite the relevant papers.

For more information, please see the OSQP website.

Installing

C

The OSQP C code can be found in the main OSQP repository, and build instructions can be found in the documentation.

Python

OSQP is available through PyPI by doing:

pip install osqp

OSQP is also available from Conda forge by doing:

conda install -c conda-forge osqp

Julia

OSQP is available in the general registry by running:

pkg> add OSQP

R

OSQP is available in CRAN by running:

install.packages("osqp")

Pinned Loading

  1. osqposqpPublic

    The Operator Splitting QP Solver

    C 2.2k 415

  2. qdldlqdldlPublic

    A free LDL factorisation routine

    C 105 44

  3. OSQP.jlOSQP.jlPublic

    Julia interface for OSQP: The Operator Splitting QP Solver

    Julia 72 25

  4. osqp-pythonosqp-pythonPublic

    Python interface for OSQP

    Python 129 50

  5. osqpthosqpthPublic

    The differentiable OSQP solver layer for PyTorch

    Python 67 6

  6. osqp-matlabosqp-matlabPublic

    Matlab interface for OSQP

    MATLAB 59 24

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Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


Pinned
Loading
  1. osqposqpPublic
    
The Operator Splitting QP Solver

    C
2.2k

415

  2. qdldlqdldlPublic
    
A free LDL factorisation routine

    C
105

44

  3. OSQP.jlOSQP.jlPublic
    
Julia interface for OSQP: The Operator Splitting QP Solver

    Julia
72

25

  4. osqp-pythonosqp-pythonPublic
    
Python interface for OSQP

    Python
129

50

  5. osqpthosqpthPublic
    
The differentiable OSQP solver layer for PyTorch

    Python
67

6

  6. osqp-matlabosqp-matlabPublic
    
Matlab interface for OSQP

    MATLAB
59

24


Repositories

Showing 10 of 24 repositories
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, 'i');
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Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


Pinned
Loading
  1. osqposqpPublic
    
The Operator Splitting QP Solver

    C
2.2k

415

  2. qdldlqdldlPublic
    
A free LDL factorisation routine

    C
105

44

  3. OSQP.jlOSQP.jlPublic
    
Julia interface for OSQP: The Operator Splitting QP Solver

    Julia
72

25

  4. osqp-pythonosqp-pythonPublic
    
Python interface for OSQP

    Python
129

50

  5. osqpthosqpthPublic
    
The differentiable OSQP solver layer for PyTorch

    Python
67

6

  6. osqp-matlabosqp-matlabPublic
    
Matlab interface for OSQP

    MATLAB
59

24


Repositories

Showing 10 of 24 repositories
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, 'i');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


Pinned
Loading
  1. osqposqpPublic
    
The Operator Splitting QP Solver

    C
2.2k

415

  2. qdldlqdldlPublic
    
A free LDL factorisation routine

    C
105

44

  3. OSQP.jlOSQP.jlPublic
    
Julia interface for OSQP: The Operator Splitting QP Solver

    Julia
72

25

  4. osqp-pythonosqp-pythonPublic
    
Python interface for OSQP

    Python
129

50

  5. osqpthosqpthPublic
    
The differentiable OSQP solver layer for PyTorch

    Python
67

6

  6. osqp-matlabosqp-matlabPublic
    
Matlab interface for OSQP

    MATLAB
59

24


Repositories

Showing 10 of 24 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


Pinned
Loading
  1. osqposqpPublic
    
The Operator Splitting QP Solver

    C
2.2k

415

  2. qdldlqdldlPublic
    
A free LDL factorisation routine

    C
105

44

  3. OSQP.jlOSQP.jlPublic
    
Julia interface for OSQP: The Operator Splitting QP Solver

    Julia
72

25

  4. osqp-pythonosqp-pythonPublic
    
Python interface for OSQP

    Python
129

50

  5. osqpthosqpthPublic
    
The differentiable OSQP solver layer for PyTorch

    Python
67

6

  6. osqp-matlabosqp-matlabPublic
    
Matlab interface for OSQP

    MATLAB
59

24


Repositories

Showing 10 of 24 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


Pinned
Loading
  1. osqposqpPublic
    
The Operator Splitting QP Solver

    C
2.2k

415

  2. qdldlqdldlPublic
    
A free LDL factorisation routine

    C
105

44

  3. OSQP.jlOSQP.jlPublic
    
Julia interface for OSQP: The Operator Splitting QP Solver

    Julia
72

25

  4. osqp-pythonosqp-pythonPublic
    
Python interface for OSQP

    Python
129

50

  5. osqpthosqpthPublic
    
The differentiable OSQP solver layer for PyTorch

    Python
67

6

  6. osqp-matlabosqp-matlabPublic
    
Matlab interface for OSQP

    MATLAB
59

24


Repositories

Showing 10 of 24 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


Pinned
Loading
  1. osqposqpPublic
    
The Operator Splitting QP Solver

    C
2.2k

415

  2. qdldlqdldlPublic
    
A free LDL factorisation routine

    C
105

44

  3. OSQP.jlOSQP.jlPublic
    
Julia interface for OSQP: The Operator Splitting QP Solver

    Julia
72

25

  4. osqp-pythonosqp-pythonPublic
    
Python interface for OSQP

    Python
129

50

  5. osqpthosqpthPublic
    
The differentiable OSQP solver layer for PyTorch

    Python
67

6

  6. osqp-matlabosqp-matlabPublic
    
Matlab interface for OSQP

    MATLAB
59

24


Repositories

Showing 10 of 24 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
@osqp

OSQP

An organization for the OSQP solver and related repositories
README.md
OSQP - The Operator Splitting QP Solver
GitHub release versionLicense
PyPI - downloadsConda - downloadsJulia - downloads
OSQP is an optimization solver for Quadratic Programs (QPs) that uses the Alternating Direction Method of Multipliers (ADMM).
It is written in pure C, is Apache-2.0 licensed, and can be compiled into a library-free embedded solver for control and robotics applications.
Interfaces to high-level languages like Python, Julia, Matlab and R are also available, and OSQP can be used from
modelling languages such as CVXPY, JuMP, and YALMIP.
Visit the documentation to learn how to use OSQP.
Visit our GitHub Discussions page for any questions related to the solver!
If you use OSQP in an academic work, please cite the relevant papers.
For more information, please see the OSQP website.
Installing
C
The OSQP C code can be found in the main OSQP repository, and build instructions
can be found in the documentation.
Python
OSQP is available through PyPI by doing:
pip install osqp

OSQP is also available from Conda forge by doing:
conda install -c conda-forge osqp

Julia
OSQP is available in the general registry by running:
pkg> add OSQP

R
OSQP is available in CRAN by running:
install.packages("osqp")


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