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- import numpy as np
- from scipy import sparse
- import utils.codegen_utils as cu
- P = sparse.triu([[4., 1.], [1., 2.]], format='csc')
- q = np.ones(2)
- A = sparse.csc_matrix(np.array([[1., 1.], [1., 0.], [0., 1.], [0., 1.]]))
- l = np.array([1., 0., 0., -np.inf])
- u = np.array([1., 0.7, 0.7, np.inf])
- n = P.shape[0]
- m = A.shape[0]
- # New data
- q_new = np.array([2.5, 3.2])
- l_new = np.array([0.8, -3.4, -np.inf, 0.5])
- u_new = np.array([1.6, 1.0, np.inf, 0.5])
- # Generate problem solutions
- sols_data = {'x_test': np.array([0.3, 0.7]),
- 'y_test': np.array([-2.9, 0.0, 0.2, 0.0]),
- 'obj_value_test': 1.88,
- 'status_test': 'optimal',
- 'q_new': q_new,
- 'l_new': l_new,
- 'u_new': u_new}
- # Generate problem data
- cu.generate_problem_data(P, q, A, l, u, 'basic_qp', sols_data)
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