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- import numpy as np
- from scipy import sparse
- import utils.codegen_utils as cu
- P = sparse.diags([1., 0.], format='csc')
- q = np.array([1., -1.])
- A12 = sparse.csc_matrix([[1., 1.], [1., 0.], [0., 1.]])
- A34 = sparse.csc_matrix([[1., 0.], [1., 0.], [0., 1.]])
- l = np.array([0., 1., 1.])
- u1 = np.array([5., 3., 3.])
- u2 = np.array([0., 3., 3.])
- u3 = np.array([2., 3., np.inf])
- u4 = np.array([0., 3., np.inf])
- # Generate problem solutions
- data = {'P': P,
- 'q': q,
- 'A12': A12,
- 'A34': A34,
- 'l': l,
- 'u1': u1,
- 'u2': u2,
- 'u3': u3,
- 'u4': u4,
- 'x1': np.array([1., 3.]),
- 'y1': np.array([0., -2., 1.]),
- 'obj_value1': -1.5,
- 'status1': 'optimal',
- 'status2': 'primal_infeasible',
- 'status3': 'dual_infeasible',
- 'status4': 'primal_infeasible'
- }
- # Generate problem data
- cu.generate_data('primal_dual_infeasibility', data)
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