I am porting a MATLAB script to Python that uses the quadprog function. The best equivalent seems to be qp. I can work out the equivalent notation with the doc and a bit of time, but I’m lazy and it would be nice to just copy-paste some code instead of thinking about it. So:
- Which of the parameters in the two functions are equivalent?
- How do I incorporate the upper and lower bounds from quadprog into cvxopt.solvers.qp?
I’ve already read this related question but I’m even lazier than that.
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Answer
This is a silly question you goose, but since there might be more lazy people around, take this line of MATLAB code (using numbered parameters for readability):
x = quadprog(C1, C2, C3, C4, C5, C6, C7, C8, ...)
The equivalent in Python will be:
import numpy import cvxopt n = C1.shape[1] # This is for readability only P = C1 q = C2 G = numpy.vstack([C3, -numpy.eye(n), numpy.eye(n)]) h = numpy.hstack([C4, -C7, C8]) A = C5 b = C6 sol = cvxopt.solvers.qp(cvxopt.matrix(P), cvxopt.matrix(q), cvxopt.matrix(G), cvxopt.matrix(h), cvxopt.matrix(A), cvxopt.matrix(b)) x = sol['x']
Assuming that you’ve been working with numpy.arrays. If you’ve been using matrices, check this doc to find this formulation for G and h:
G = numpy.bmat('C3, -numpy.eye(n), numpy.eye(n)')
h = numpy.bmat('C4, -C7, C8')