def solve_unbounded(use_indirect, gpu):
K = {
'f': 10,
'l': 15,
'q': [5, 10, 0, 1],
's': [3, 4, 0, 0, 1],
'ep': 10,
'ed': 10,
'p': [-0.25, 0.5, 0.75, -0.33]
}
m = tools.get_scs_cone_dims(K)
data = tools.gen_unbounded(K, n=m // 3)
params = {'normalize': True, 'scale': 0.5, 'cg_rate': 2}
sol = scs.solve(data, K, use_indirect=use_indirect, gpu=gpu, **params)
def solve_unbounded(use_indirect, gpu):
K = {
"f": 10,
"l": 15,
"q": [5, 10, 0, 1],
"s": [3, 4, 0, 0, 1],
"ep": 10,
"ed": 10,
"p": [-0.25, 0.5, 0.75, -0.33],
}
m = tools.get_scs_cone_dims(K)
data = tools.gen_unbounded(K, n=m // 3)
params = {"normalize": True, "scale": 0.5}
sol = scs.solve(data, K, use_indirect=use_indirect, gpu=gpu, **params)
def solve_feasible(use_indirect, gpu):
# cone:
K = {
'f': 10,
'l': 15,
'q': [5, 10, 0, 1],
's': [3, 4, 0, 0, 1],
'ep': 10,
'ed': 10,
'p': [-0.25, 0.5, 0.75, -0.33]
}
m = tools.get_scs_cone_dims(K)
data, p_star = tools.gen_feasible(K, n=m // 3, density=0.01)
params = {'normalize': True, 'scale': 5, 'cg_rate': 2}
sol = scs.solve(data, K, use_indirect=use_indirect, gpu=gpu, **params)
x = sol['x']
y = sol['y']
print('p* = ', p_star)
print('pri error = ', (np.dot(data['c'], x) - p_star) / p_star)
print('dual error = ', (-np.dot(data['b'], y) - p_star) / p_star)
def solve_feasible(use_indirect, gpu):
# cone:
K = {
"f": 10,
"l": 15,
"q": [5, 10, 0, 1],
"s": [3, 4, 0, 0, 1],
"ep": 10,
"ed": 10,
"p": [-0.25, 0.5, 0.75, -0.33],
}
m = tools.get_scs_cone_dims(K)
data, p_star = tools.gen_feasible(K, n=m // 3, density=0.01)
params = {"normalize": True, "scale": 5}
sol = scs.solve(data, K, use_indirect=use_indirect, gpu=gpu, **params)
x = sol["x"]
y = sol["y"]
print("p* = ", p_star)
print("pri error = ", (np.dot(data["c"], x) - p_star) / p_star)
print("dual error = ", (-np.dot(data["b"], y) - p_star) / p_star)
num_infeas = 10
opts = {
'max_iters': 100000,
'eps': 1e-5
} # better accuracy than default to ensure test pass
K = {
'f': 10,
'l': 25,
'q': [5, 10, 0, 1],
's': [2, 1, 2, 0, 1],
'ep': 0,
'ed': 0,
'p': [0.25, -0.75, 0.33, -0.33, 0.2]
}
m = tools.get_scs_cone_dims(K)
def test_feasible():
for i in range(num_feas):
data, p_star = tools.gen_feasible(K, n=m // 3, density=0.1)
sol = scs.solve(data, K, use_indirect=False, **opts)
yield check_solution, np.dot(data['c'], sol['x']), p_star
yield check_solution, np.dot(-data['b'], sol['y']), p_star
sol = scs.solve(data, K, use_indirect=True, **opts)
yield check_solution, np.dot(data['c'], sol['x']), p_star
yield check_solution, np.dot(-data['b'], sol['y']), p_star
