Example Problems Osqp Osqp Deepwiki

Leo Migdal

-Nov 17, 2025, 12:34 AM

This page provides an overview and detailed examples of how to use OSQP to solve various quadratic programming problems. The examples range from basic usage patterns to advanced real-world applications, demonstrating OSQP's versatility and efficiency. The examples are presented with mathematical formulations and code implementations in multiple programming languages, including Python, MATLAB, Julia, C, and R, as well as through high-level modeling frameworks like CVXPY and YALMIP. For information about the solver's settings and configuration options, see the Solver Settings page. Sources: docs/interfaces/index.rst docs/examples/huber.rst docs/examples/lasso.rst docs/examples/mpc.rst docs/examples/least-squares.rst docs/examples/svm.rst docs/examples/portfolio.rst docs/examples/setup-and-solve.rst docs/examples/update-vectors.rst docs/examples/update-matrices.rst Sources: docs/solver/index.rst docs/interfaces/python.rst docs/interfaces/matlab.rst docs/interfaces/julia.rst

This example demonstrates the fundamental workflow of setting up and solving a simple QP: This page documents basic usage examples of the OSQP (Operator Splitting Quadratic Program Solver) library. It covers fundamental operations such as setting up and solving quadratic programming problems, updating problem data efficiently, and applying OSQP to common optimization scenarios. For more advanced applications of OSQP, see Advanced Applications. OSQP solves convex quadratic programs in the following form: The following diagram illustrates the basic workflow for solving a quadratic program with OSQP:

Sources: examples/osqp_demo.c30-204 docs/examples/setup-and-solve.rst1-208 This is a standard form QP with 2 variables and 3 constraints. Here's how to set up and solve this problem in different languages: There was an error while loading. Please reload this page. We show below how to solve the problem in Python, Matlab, Julia and C.

This page showcases advanced real-world applications of OSQP beyond basic quadratic programming. It demonstrates how OSQP can be used to solve complex optimization problems in control systems, finance, machine learning, and other fields. For basic usage examples, see Basic Examples. OSQP efficiently solves a variety of complex optimization problems that arise in real-world applications. These problems often involve specialized formulations that can be transformed into quadratic programs. Model Predictive Control is a popular advanced control strategy used in industries ranging from autonomous vehicles to chemical processing plants.

OSQP is particularly well-suited for MPC applications due to its performance, ability to handle constraints, and warm-starting capabilities. MPC involves solving a quadratic program at each time step to determine the optimal control inputs. The key steps include: Problem Formulation: The dynamics and constraints are formulated as a QP: There was an error while loading. Please reload this page.

I was looking for resources to find examples where the CUDA back-end beats the CPU, unfortunately couldn't find any. I was wondering if anyone could provide examples/code for such problems. Beta Was this translation helpful? Give feedback. @Eqedos - the repo osqp-benchmarks has a lot of sample problems. In particular, the Huber class of problems benefit from the CUDA backend, for higher dimensions of problems.

See a benchmark graphic below (blue is built-in backend, orange is cuda backend): A sample standalone script representing the Huber problem would be: This document provides a comprehensive reference for the OSQP (Operator Splitting Quadratic Program) solver's public API. It covers the main data structures, functions, and constants that allow users to set up, solve, and update quadratic programming problems. For information about the underlying ADMM algorithm, see ADMM Algorithm. For details on linear algebra operations, see Linear Algebra Operations.

The OSQP API follows a consistent pattern for solving quadratic programming problems of the form: Sources: src/osqp_api.c331-631 include/public/osqp_api_functions.h242-317 Main structure containing the solver state, settings, and results. Sources: include/public/osqp_api_types.h181-187 In this example we show how to construct and solve a differentiable QP problem using PyTorch and the osqp.nn.torch.OSQP differentiable QP solver. There was an error while loading.

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Example Problems Osqp Osqp Deepwiki

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