| 1234567891011121314151617 |
- 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)
|
