qpsolvers

import numpy as np from qpsolvers import solve_qp, available_solvers # Define the QP problem in standard form: minimize 0.5 * x.T * P * x + q.T * x # subject to G * x <= h, A * x == b, lb <= x <= ub M = np.array([[1., 2., 0.], [-8., 3., 2.], [0., 1., 1.]]) P = M.T @ M # This results in a positive definite matrix q = np.array([3., 2., 3.]) @ M G = np.array([[1., 2., 1.], [2., 0., 1.], [-1., 2., -1.]]) h = np.array([3., 2., -2.]) A = np.array([1., 1., 1.]) b = np.array([1.]) # Try to use a common open-source solver, or fallback if not available solver_to_use = "proxqp" if "proxqp" in available_solvers else available_solvers[0] if available_solvers else None if solver_to_use: print(f"Using solver: {solver_to_use}") x = solve_qp(P, q, G, h, A, b, solver=solver_to_use) print(f"QP solution: {x = }") else: print("No QP solvers found. Please install one, e.g., pip install qpsolvers[proxqp]")