Python

Import

The OSQP module is can be imported with

import osqp

Setup

The solver is initialized by creating an OSQP object

m = osqp.OSQP()

The problem is specified in the setup phase by running

m.setup(P=P, q=q, A=A, l=l, u=u, **settings)

The arguments q, l and u are numpy arrays. The elements of l and u can be \(\pm \infty\) ( using numpy.inf).

The arguments P and A are scipy sparse matrices in CSC format. If they are sparse matrices are in another format, the interface will attemp to convert them. There is no need to specify all the arguments.

The keyword arguments **settings specify the solver settings. The allowed parameters are defined in Solver settings.

Solve

The problem can be solved by

results = m.solve()

The results object contains the primal solution x, the dual solution y, certificate of primal infeasibility prim_inf_cert, certificate of dual infeasibility dual_inf_cert and the info object containing the solver statistics defined in the following table

Member Description
iter Number of iterations
status Solver status
status_val Solver status value as in Status values
status_polish Polishing status
obj_val Objective value
pri_res Primal residual
dua_res Dual residual
setup_time Setup time
solve_time Solve time
polish_time Polish time
run_time Total run time: setup + solve + polish
rho_estimate Optimal rho estimate
rho_updates Number of rho updates

Note that if multiple solves are executed from single setup, then after the first one run_time includes only solve_time + polish_time.

Update

Part of problem data and settings can be updated without requiring a new problem setup.

Update problem vectors

Vectors q, l and u can be updated with new values q_new, l_new and u_new by just running

m.update(q=q_new, l=l_new, u=u_new)

The user does not have to specify all the keyword arguments.

Update settings

Settings can be updated by running

m.update_settings(**kwargs)

where kwargs are the settings that can be updated which are marked with an * in Solver settings.

Warm start

OSQP automatically warm starts primal and dual variables from the previous QP solution. If you would like to warm start their values manually, you can use

m.warm_start(x=x0, y=y0)

where x0 and y0 are the new primal and dual variables.