Cvxopt¶
Cvxopt provides many routines for solving convex optimization
problems such as linear and quadratic programming packages. It also
has a very nice sparse matrix library that provides an interface to
umfpack (the same sparse matrix solver that matlab uses), it also
has a nice interface to lapack. For more details on cvxopt please
refer to its documentation at http://cvxopt.org/userguide/index.html
Sparse matrices are represented in triplet notation that is as a list of nonzero values, row indices and column indices. This is internally converted to compressed sparse column format. So for example we would enter the matrix
\[\begin{split}\left(
\begin{array}{ccccc}
2&3&0&0&0\\
3&0&4&0&6\\
0&-1&-3&2&0\\
0&0&1&0&0\\
0&4&2&0&1
\end{array}\right)\end{split}\]
by
sage: # needs cvxopt
sage: import numpy
sage: from cvxopt.base import spmatrix
sage: from cvxopt.base import matrix as m
sage: from cvxopt import umfpack
sage: Integer = int
sage: V = [2,3, 3,-1,4, 4,-3,1,2, 2, 6,1]
sage: I = [0,1, 0, 2,4, 1, 2,3,4, 2, 1,4]
sage: J = [0,0, 1, 1,1, 2, 2,2,2, 3, 4,4]
sage: A = spmatrix(V,I,J)
>>> from sage.all import *
>>> # needs cvxopt
>>> import numpy
>>> from cvxopt.base import spmatrix
>>> from cvxopt.base import matrix as m
>>> from cvxopt import umfpack
>>> Integer = int
>>> V = [Integer(2),Integer(3), Integer(3),-Integer(1),Integer(4), Integer(4),-Integer(3),Integer(1),Integer(2), Integer(2), Integer(6),Integer(1)]
>>> I = [Integer(0),Integer(1), Integer(0), Integer(2),Integer(4), Integer(1), Integer(2),Integer(3),Integer(4), Integer(2), Integer(1),Integer(4)]
>>> J = [Integer(0),Integer(0), Integer(1), Integer(1),Integer(1), Integer(2), Integer(2),Integer(2),Integer(2), Integer(3), Integer(4),Integer(4)]
>>> A = spmatrix(V,I,J)
# needs cvxopt import numpy from cvxopt.base import spmatrix from cvxopt.base import matrix as m from cvxopt import umfpack Integer = int V = [2,3, 3,-1,4, 4,-3,1,2, 2, 6,1] I = [0,1, 0, 2,4, 1, 2,3,4, 2, 1,4] J = [0,0, 1, 1,1, 2, 2,2,2, 3, 4,4] A = spmatrix(V,I,J)
To solve an equation \(AX=B\), with \(B=[1,1,1,1,1]\), we could do the following.
sage: B = numpy.array([1.0]*5) # needs cvxopt
sage: B.shape=(5,1) # needs cvxopt
sage: print(B) # needs cvxopt
[[1.]
[1.]
[1.]
[1.]
[1.]]
sage: # needs cvxopt
sage: print(A)
[ 2.00e+00 3.00e+00 0 0 0 ]
[ 3.00e+00 0 4.00e+00 0 6.00e+00]
[ 0 -1.00e+00 -3.00e+00 2.00e+00 0 ]
[ 0 0 1.00e+00 0 0 ]
[ 0 4.00e+00 2.00e+00 0 1.00e+00]
sage: C = m(B)
sage: umfpack.linsolve(A,C)
sage: print(C)
[ 5.79e-01]
[-5.26e-02]
[ 1.00e+00]
[ 1.97e+00]
[-7.89e-01]
>>> from sage.all import *
>>> B = numpy.array([RealNumber('1.0')]*Integer(5)) # needs cvxopt
>>> B.shape=(Integer(5),Integer(1)) # needs cvxopt
>>> print(B) # needs cvxopt
[[1.]
[1.]
[1.]
[1.]
[1.]]
>>> # needs cvxopt
>>> print(A)
[ 2.00e+00 3.00e+00 0 0 0 ]
[ 3.00e+00 0 4.00e+00 0 6.00e+00]
[ 0 -1.00e+00 -3.00e+00 2.00e+00 0 ]
[ 0 0 1.00e+00 0 0 ]
[ 0 4.00e+00 2.00e+00 0 1.00e+00]
>>> C = m(B)
>>> umfpack.linsolve(A,C)
>>> print(C)
[ 5.79e-01]
[-5.26e-02]
[ 1.00e+00]
[ 1.97e+00]
[-7.89e-01]
B = numpy.array([1.0]*5) # needs cvxopt B.shape=(5,1) # needs cvxopt print(B) # needs cvxopt # needs cvxopt print(A) C = m(B) umfpack.linsolve(A,C) print(C)
Note the solution is stored in \(B\) afterward. also note the
m(B), this turns our numpy array into a format cvxopt understands.
You can directly create a cvxopt matrix using cvxopt’s own matrix
command, but I personally find numpy arrays nicer. Also note we
explicitly set the shape of the numpy array to make it clear it was
a column vector.
We could compute the approximate minimum degree ordering by doing
sage: # needs cvxopt
sage: RealNumber = float
sage: Integer = int
sage: from cvxopt.base import spmatrix
sage: from cvxopt import amd
sage: A = spmatrix([10,3,5,-2,5,2],[0,2,1,2,2,3],[0,0,1,1,2,3])
sage: P = amd.order(A)
sage: print(P)
[ 1]
[ 0]
[ 2]
[ 3]
>>> from sage.all import *
>>> # needs cvxopt
>>> RealNumber = float
>>> Integer = int
>>> from cvxopt.base import spmatrix
>>> from cvxopt import amd
>>> A = spmatrix([Integer(10),Integer(3),Integer(5),-Integer(2),Integer(5),Integer(2)],[Integer(0),Integer(2),Integer(1),Integer(2),Integer(2),Integer(3)],[Integer(0),Integer(0),Integer(1),Integer(1),Integer(2),Integer(3)])
>>> P = amd.order(A)
>>> print(P)
[ 1]
[ 0]
[ 2]
[ 3]
# needs cvxopt RealNumber = float Integer = int from cvxopt.base import spmatrix from cvxopt import amd A = spmatrix([10,3,5,-2,5,2],[0,2,1,2,2,3],[0,0,1,1,2,3]) P = amd.order(A) print(P)
For a simple linear programming example, if we want to solve
\[\begin{split}\begin{array}{cc}
\text{minimize} & -4x_1-5x_2\\
\text{subject to} & 2x_1 +x_2\le 3\\
& x_1+2x_2\le 3\\
& x_1 \ge 0 \\
& x_2 \ge 0\\
\end{array}\end{split}\]
sage: # needs cvxopt
sage: RealNumber = float
sage: Integer = int
sage: from cvxopt.base import matrix as m
sage: from cvxopt import solvers
sage: c = m([-4., -5.])
sage: G = m([[2., 1., -1., 0.], [1., 2., 0., -1.]])
sage: h = m([3., 3., 0., 0.])
sage: sol = solvers.lp(c,G,h) # random
pcost dcost gap pres dres k/t
0: -8.1000e+00 -1.8300e+01 4e+00 0e+00 8e-01 1e+00
1: -8.8055e+00 -9.4357e+00 2e-01 1e-16 4e-02 3e-02
2: -8.9981e+00 -9.0049e+00 2e-03 1e-16 5e-04 4e-04
3: -9.0000e+00 -9.0000e+00 2e-05 3e-16 5e-06 4e-06
4: -9.0000e+00 -9.0000e+00 2e-07 1e-16 5e-08 4e-08
>>> from sage.all import *
>>> # needs cvxopt
>>> RealNumber = float
>>> Integer = int
>>> from cvxopt.base import matrix as m
>>> from cvxopt import solvers
>>> c = m([-RealNumber('4.'), -RealNumber('5.')])
>>> G = m([[RealNumber('2.'), RealNumber('1.'), -RealNumber('1.'), RealNumber('0.')], [RealNumber('1.'), RealNumber('2.'), RealNumber('0.'), -RealNumber('1.')]])
>>> h = m([RealNumber('3.'), RealNumber('3.'), RealNumber('0.'), RealNumber('0.')])
>>> sol = solvers.lp(c,G,h) # random
pcost dcost gap pres dres k/t
0: -8.1000e+00 -1.8300e+01 4e+00 0e+00 8e-01 1e+00
1: -8.8055e+00 -9.4357e+00 2e-01 1e-16 4e-02 3e-02
2: -8.9981e+00 -9.0049e+00 2e-03 1e-16 5e-04 4e-04
3: -9.0000e+00 -9.0000e+00 2e-05 3e-16 5e-06 4e-06
4: -9.0000e+00 -9.0000e+00 2e-07 1e-16 5e-08 4e-08
# needs cvxopt RealNumber = float Integer = int from cvxopt.base import matrix as m from cvxopt import solvers c = m([-4., -5.]) G = m([[2., 1., -1., 0.], [1., 2., 0., -1.]]) h = m([3., 3., 0., 0.]) sol = solvers.lp(c,G,h) # random
sage: print(sol['x']) # ... below since can get -00 or +00 depending on architecture # needs cvxopt
[ 1.00e...00]
[ 1.00e+00]
>>> from sage.all import *
>>> print(sol['x']) # ... below since can get -00 or +00 depending on architecture # needs cvxopt
[ 1.00e...00]
[ 1.00e+00]
print(sol['x']) # ... below since can get -00 or +00 depending on architecture # needs cvxopt