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)
# Set random seed for reproducibility
rg = Generator(PCG64(2))
n = 50
m = 150
# Generate random Matrices
Pt = sparse.random(n, n, random_state=rg)
P = Pt.T.dot(Pt) + sparse.eye(n)
P = sparse.triu(P, format='csc')
q = sp.randn(n)
A = sparse.random(m, n, random_state=rg).tolil() # Lil for efficiency
u = 3 + sp.randn(m)
l = -3 + sp.randn(m)
# Make random problem primal infeasible
A[int(n/2), :] = A[int(n/2)+1, :]
l[int(n/2)] = u[int(n/2)+1] + 10 * rg.random()
u[int(n/2)] = l[int(n/2)] + 0.5
# Convert A to csc
A = A.tocsc()
# Generate problem solutions
sols_data = {'status_test': 'primal_infeasible'}
# Generate problem data
cu.generate_problem_data(P, q, A, l, u, 'primal_infeasibility', sols_data)
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)
n = 50
m = 100
N = int(m / 2)
gamma = 1.0
b = np.hstack([np.ones(N), -np.ones(N)])
A_upp = sparse.random(N, n, density=0.5)
A_low = sparse.random(N, n, density=0.5)
Ad = sparse.vstack(
[
A_upp / np.sqrt(n) + (A_upp != 0.0).astype(float) / n,
A_low / np.sqrt(n) - (A_low != 0.0).astype(float) / n,
],
format="csc",
)
# osqp data
Im = sparse.eye(m)
P = sparse.block_diag([sparse.eye(n), sparse.csc_matrix((m, m))], format="csc")
q = np.hstack([np.zeros(n), gamma * np.ones(m)])
A = sparse.vstack(
[
sparse.hstack([sparse.diags(b).dot(Ad), -Im]),
sparse.hstack([sparse.csc_matrix((m, n)), Im]),
],
format="csc",
)
l = np.hstack([-np.inf * np.ones(m), np.zeros(m)])
u = np.hstack([-np.ones(m), np.inf * np.ones(m)])
cu.generate_problem_data(P, q, A, l, u, "svm")
import numpy as np
import scipy.sparse as spa
import utils.codegen_utils as cu
P = spa.csc_matrix(np.array([[2., 5.], [5., 1.]]))
q = np.array([3., 4.])
A = spa.csc_matrix(
np.array([[-1.0, 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)
import numpy as np
import scipy.sparse as spa
import utils.codegen_utils as cu
P = spa.csc_matrix(np.array([[2., 5.], [5., 1.]]))
q = np.array([3., 4.])
A = spa.csc_matrix(
np.array([[-1.0, 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.])
# Generate problem data
cu.generate_problem_data(P, q, A, l, u, 'non_cvx')
