large scale optimization with Python

Question

I am dealing with the following optimization problem: $$ \underset{x}{\min} q(x) $$ subject to $$ l_{x} \leq x \leq u_{x} \,\,\,\, \text{ and } \,\,\,\, l_{a} \leq Ax \leq u_{a}. $$ where $q(x)$ is a quadratic objective function.

In my case the size of vector $x$ is large, but the matrix $A$ is very sparse. One of the ways to deal with this is SQOPT algorithm, but the implementation is only on MATLAB.

I wonder which Python library can I try instead of SQOPT?

Answer 1

Quadratic programming solvers in Python with a unified API (here) includes most of the quadratic programming solvers such as CVXOPT (can take advantage of sparsity), Gurobi, MOSEK, OSQP, etc.

The performance comparison of these solvers (at least some of them) can be found here.