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3

import numpy as np
import pandas as pd
import cvxpy as cp
import cvxopt
from cvxopt import matrix
G_ = np.load('/shared/FactorBank/temp/G.npy')
h_ = np.load('/shared/FactorBank/temp/h.npy')
q_ = np.load('/shared/FactorBank/temp/q.npy')
N = len(q_)

This is the part of cvxpy.

h = h_.reshape(len(h_))
A = np.ones((1, N))
b = np.ones(1)
x = cp.Variable(int(N))
obj = cp.sum(q_ * x)
#print(G_)
constraints = [G_ * x <= h, A * x == b]
prob = cp.Problem(cp.Minimize(obj), constraints)
prob.solve()
print "status:", prob.status
print "optimal value", prob.value 
print "optimal var", x.value

status: optimal

optimal value -3.598688474475655

optimal var [ 1.45281688e-05 1.21958458e-05 -2.80709993e-04 ... -1.77801870e-04
-2.01577984e-04 -1.12303384e-04]

This is the part of cvxopt.

G = matrix(G_)
h = matrix(h_)
A = matrix(1.0, (1, N))
b = matrix(1.0)
q = matrix(q_.T)
portfolios = cvxopt.solvers.lp(q, G, h, A, b)

pcost dcost gap pres dres k/t

0: 2.8090e-02 -3.1698e+02 2e+04 2e-01 5e-09 1e+00

1: -6.1531e+01 -9.3943e+01 2e+03 2e-02 6e-10 2e-02

2: -2.7578e+01 -3.3144e+01 1e+02 3e-03 1e-10 2e-02

3: -1.8330e+01 -2.1642e+01 7e+01 2e-03 6e-11 1e-02

……

22: -2.4301e+00 -2.4301e+00 5e-06 1e-10 3e-11 7e-10

23: -2.4301e+00 -2.4301e+00 8e-08 2e-12 1e-09 1e-11
Optimal solution found.

So does anyone know although the status are both "optimal", why the results are not the same?

python

share|improve this question

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge

add a comment | 

3

import numpy as np
import pandas as pd
import cvxpy as cp
import cvxopt
from cvxopt import matrix
G_ = np.load('/shared/FactorBank/temp/G.npy')
h_ = np.load('/shared/FactorBank/temp/h.npy')
q_ = np.load('/shared/FactorBank/temp/q.npy')
N = len(q_)

This is the part of cvxpy.

h = h_.reshape(len(h_))
A = np.ones((1, N))
b = np.ones(1)
x = cp.Variable(int(N))
obj = cp.sum(q_ * x)
#print(G_)
constraints = [G_ * x <= h, A * x == b]
prob = cp.Problem(cp.Minimize(obj), constraints)
prob.solve()
print "status:", prob.status
print "optimal value", prob.value 
print "optimal var", x.value

status: optimal

optimal value -3.598688474475655

optimal var [ 1.45281688e-05 1.21958458e-05 -2.80709993e-04 ... -1.77801870e-04
-2.01577984e-04 -1.12303384e-04]

This is the part of cvxopt.

G = matrix(G_)
h = matrix(h_)
A = matrix(1.0, (1, N))
b = matrix(1.0)
q = matrix(q_.T)
portfolios = cvxopt.solvers.lp(q, G, h, A, b)

pcost dcost gap pres dres k/t

0: 2.8090e-02 -3.1698e+02 2e+04 2e-01 5e-09 1e+00

1: -6.1531e+01 -9.3943e+01 2e+03 2e-02 6e-10 2e-02

2: -2.7578e+01 -3.3144e+01 1e+02 3e-03 1e-10 2e-02

3: -1.8330e+01 -2.1642e+01 7e+01 2e-03 6e-11 1e-02

……

22: -2.4301e+00 -2.4301e+00 5e-06 1e-10 3e-11 7e-10

23: -2.4301e+00 -2.4301e+00 8e-08 2e-12 1e-09 1e-11
Optimal solution found.

So does anyone know although the status are both "optimal", why the results are not the same?

python

share|improve this question

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge

add a comment | 

import numpy as np
import pandas as pd
import cvxpy as cp
import cvxopt
from cvxopt import matrix
G_ = np.load('/shared/FactorBank/temp/G.npy')
h_ = np.load('/shared/FactorBank/temp/h.npy')
q_ = np.load('/shared/FactorBank/temp/q.npy')
N = len(q_)

This is the part of cvxpy.

h = h_.reshape(len(h_))
A = np.ones((1, N))
b = np.ones(1)
x = cp.Variable(int(N))
obj = cp.sum(q_ * x)
#print(G_)
constraints = [G_ * x <= h, A * x == b]
prob = cp.Problem(cp.Minimize(obj), constraints)
prob.solve()
print "status:", prob.status
print "optimal value", prob.value 
print "optimal var", x.value

status: optimal

optimal value -3.598688474475655

optimal var [ 1.45281688e-05 1.21958458e-05 -2.80709993e-04 ... -1.77801870e-04
-2.01577984e-04 -1.12303384e-04]

This is the part of cvxopt.

G = matrix(G_)
h = matrix(h_)
A = matrix(1.0, (1, N))
b = matrix(1.0)
q = matrix(q_.T)
portfolios = cvxopt.solvers.lp(q, G, h, A, b)

pcost dcost gap pres dres k/t

0: 2.8090e-02 -3.1698e+02 2e+04 2e-01 5e-09 1e+00

1: -6.1531e+01 -9.3943e+01 2e+03 2e-02 6e-10 2e-02

2: -2.7578e+01 -3.3144e+01 1e+02 3e-03 1e-10 2e-02

3: -1.8330e+01 -2.1642e+01 7e+01 2e-03 6e-11 1e-02

……

22: -2.4301e+00 -2.4301e+00 5e-06 1e-10 3e-11 7e-10

23: -2.4301e+00 -2.4301e+00 8e-08 2e-12 1e-09 1e-11
Optimal solution found.

So does anyone know although the status are both "optimal", why the results are not the same?

python

share|improve this question

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge

import numpy as np
import pandas as pd
import cvxpy as cp
import cvxopt
from cvxopt import matrix
G_ = np.load('/shared/FactorBank/temp/G.npy')
h_ = np.load('/shared/FactorBank/temp/h.npy')
q_ = np.load('/shared/FactorBank/temp/q.npy')
N = len(q_)

h = h_.reshape(len(h_))
A = np.ones((1, N))
b = np.ones(1)
x = cp.Variable(int(N))
obj = cp.sum(q_ * x)
#print(G_)
constraints = [G_ * x <= h, A * x == b]
prob = cp.Problem(cp.Minimize(obj), constraints)
prob.solve()
print "status:", prob.status
print "optimal value", prob.value 
print "optimal var", x.value

G = matrix(G_)
h = matrix(h_)
A = matrix(1.0, (1, N))
b = matrix(1.0)
q = matrix(q_.T)
portfolios = cvxopt.solvers.lp(q, G, h, A, b)

share|improve this question

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge

share|improve this question

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge

asked Mar 26 at 9:08

ArianaAriana

1611 bronze badge