| import numpy as np |
| from scipy import sparse |
| import utils.codegen_utils as cu |
| |
| P = sparse.diags([0.617022, 0.92032449, 0.20011437, 0.50233257, 0.34675589], format='csc') |
| q = np.array([-1.10593508, -1.65451545, -2.3634686, 1.13534535, -1.01701414]) |
| A = sparse.csc_matrix((0,5)) |
| l = np.array([]) |
| u = np.array([]) |
| |
| # Generate problem solutions |
| sols_data = {'x_test': np.array([1.79237542, 1.79775228, 11.81058885, -2.26014678, 2.93293975]), |
| 'obj_value_test': -19.209752026813277, |
| 'status_test': 'optimal'} |
| |
| # Generate problem data |
| cu.generate_problem_data(P, q, A, l, u, 'unconstrained', sols_data) |