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- import numpy as np
- from scipy import sparse
- import utils.codegen_utils as cu
- P = sparse.triu([[2., 5.], [5., 1.]], format='csc')
- q = np.array([3., 4.])
- A = sparse.csc_matrix([[-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.])
- sols_data = {'sigma_new': 5}
- # Generate problem data
- cu.generate_problem_data(P, q, A, l, u, 'non_cvx', sols_data)
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