| 12345678910111213141516171819202122232425262728293031323334 |
- import numpy as np
- from scipy import sparse
- import utils.codegen_utils as cu
- P = sparse.triu([[11., 0.], [0., 0.]], format='csc')
- q = np.array([3., 4.])
- A = sparse.csc_matrix(np.array([[-1., 0.], [0., -1.], [-1., 3.],
- [2., 5.], [3., 4]]))
- l = -np.inf * np.ones(A.shape[0])
- u = np.array([0., 0., -15., 100., 80.])
- n = P.shape[0]
- m = A.shape[0]
- # New data
- q_new = np.array([1., 1.])
- u_new = np.array([-2., 0., -20., 100., 80.])
- # Generate problem solutions
- sols_data = {'x_test': np.array([15., -0.]),
- 'y_test': np.array([0., 508., 168., 0., 0.]),
- 'obj_value_test': 1282.5,
- 'status_test': 'optimal',
- 'q_new': q_new,
- 'u_new': u_new,
- 'x_test_new': np.array([20., -0.]),
- 'y_test_new': np.array([0., 664., 221., 0., 0.]),
- 'obj_value_test_new': 2220.0,
- 'status_test_new': 'optimal'}
- # Generate problem data
- cu.generate_problem_data(P, q, A, l, u, 'basic_qp2', sols_data)
|
