![]() True if the solution was found successfully by a solver, False if the Solution returned by a QP solver for a given problem. Solution ( problem, extras=, found=None, obj=None, x=None, y=None, z=None, z_box=None ) # Why a solution is optimal if and only if these three residuals are zero. You can check out for an overview of optimality conditions and Solution is optimal - Primal residual : 1.1e-16 - Dual residual : 1.4e-14 - Duality gap : 0.0e 00 Optimality of the solution returned by a solver with: It is linked to the corresponding Problem, which itĬan use for instance to check residuals. The Solution class describes the solution found by a solver to a Optimality conditions and numerical tolerances in QP solvers. Introduction to dual multipliers you can also check out this post on See the examples/ folder in the repository for more use cases. Problem is non-convex and the solver fails because of that, then a Problem is non-convex, as some solvers don’t check for this. There is no guarantee that a ValueError is raised if the provided See the Supported solvers pageįor details on the parameters available to each solver. For example, we can call OSQP with a custom absoluteįeasibility tolerance by solve_problem(problem, solver='osqp', eps_abs=1e-6). Requires a definite cost matrix but the provided matrix \(P\) isĮxtra keyword arguments given to this function are forwarded to the ValueError – If the problem is not correctly defined. ![]() SolverNotFound – If the requested solver is not in qpsolvers.available_solvers. Solution found by the solver, if any, along with solver-specific return If you use CVXPY in industry, we'd love to hear from you as well, on Discord or over email.\[\begin (P If you use CVXPY for academic work, we encourage you to cite our papers. Jaehyun Park, Enzo Busseti, AJ Friend, Judson Wilson, and Chris Dembia.įor more information about the team and our processes, see our governance document. Years includes Stephen Boyd, Eric Chu, Robin Verschueren, Michael Sommerauer, A non-exhaustive list of people who have shaped CVXPY over the TeamĬVXPY is a community project, built from the contributions of manyĬVXPY is developed and maintained by StevenĪnd Bartolomeo Stellato, with many others contributing Please get in touch with us first to make sure that your priorities align withĬontributions should be submitted as pull requests.Ī member of the CVXPY development team will review the pull request and guideīefore starting work on your contribution, please read the contributing guide. If you'd like to add a new example to our library, or implement a new feature, Browse the issue tracker, and look for issues tagged as "help wanted".Read the CVXPY source code and improve the documentation, or address TODOs.Here are some simple ways to start contributing immediately: Please be respectful in your communications with the CVXPY community, and make sure to abide by our code of conduct. ![]() ![]() To share feature requests and bug reports, use Github Issues.To have longer, in-depth discussions with the CVXPY community, use Github Discussions.To chat with the CVXPY community in real-time, join us on Discord.The CVXPY community consists of researchers, data scientists, software engineers, and students from all over the world. We welcome all kinds of issues, especially those related to correctness, documentation, performance, and feature requests.įor basic usage questions (e.g., "Why isn't my problem DCP?"), please use StackOverflow instead. ![]() We encourage you to report issues using the Github tracker. To get started with CVXPY, check out the following: InstallationĬVXPY is available on PyPI, and can be installed withįor detailed instructions, see the installation Many people, across many institutions and countries. It relies upon the open source solversĬVXPY began as a Stanford University research project. mixed-integer convex optimization problems,ĬVXPY is not a solver.# The optimal Lagrange multiplier for a constraint # is stored in constraint.dual_value. # The optimal value for x is stored in x.value. # The optimal objective is returned by prob.solve(). Import cvxpy as cp import numpy # Problem data. ![]()
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