# 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

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)
```

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]
```

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]
```

For a simple linear programming example, if we want to solve

```
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
```

```
sage: print(sol['x']) # ... below since can get -00 or +00 depending on architecture # needs cvxopt
[ 1.00e...00]
[ 1.00e+00]
```