CODEWITHSUNDEEP
pythonconvex-optimization
user674155
user674155

Reputation:

cvxopt cone programming documentation

I'm trying to solve a quadratic optimisation problem using the cvxopt python library. I have quadratic constraints which I'm converting to cone constraints.

My question is regarding the cvxopt documentation for cone programming. The general cone program is described with (among others) the constraints:

s_0 => 0

s_k0 => || s_k1 || for k = 1,...,M

Do they really mean this?

They give an example under the documentation:

enter image description here

that they represent as:

G = [ matrix( [[12., 13., 12.], [6., -3., -12.], [-5., -5., 6.]] ) ]
G += [ matrix( [[3., 3., -1., 1.], [-6., -6., -9., 19.], [10., -2., -2., -3.]] ) ]
h = [ matrix( [-12., -3., -2.] ),  matrix( [27., 0., 3., -42.] ) ]

which seems to suggest that the constraints are instead:

s_k0 => || s_k1 || for k = 0,...,M

Upvotes: 0

Views: 575

Answers (1)

tynn
tynn

Reputation: 39843

You've missed the important part of the example:

sol = solvers.socp(c, Gq = G, hq = h)

From the documentation for cone programming:

The argument Gq is a list of M dense or sparse matrices G_1, ..., G_M. The argument hq is a list of M dense single-column matrices h_1, ..., h_M. The elements of Gq and hq must have at least one row. The default values of Gq and hq are empty lists.

So the componentwise inequalities represented by s_0 are just omitted.

Upvotes: 1

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