This is a pure Julia implementation of the OSQP solver available on Github. For more information read the documentation osqp.readthedocs.io.
The solver can handle problems of the form
minimize 1/2 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 in R^m U {-inf}^m
, u in R^m U {+inf}^m
.
The solver can be used by including osqp_julia.jl into your project. A short example is provided below:
# include solver file
include("osqp_julia.jl")
using OSQPSolver
# define sample problem
P = [4.0 1.0; 1.0 2.0]
q = [1.0;1.0]
A = [1.0 1.0; 1.0 0.0; 0.0 1.0]
l = [1.0;0.0;0.0]
u = [1.0;0.7;0.7]
settings = qpSettings(rho=1.0,verbose=true)
# solve QP problem
res = solveOSQP(P,q,A,l,u,settings)
Send an email to Michael Garstka