How to set up multiple equality constraints in quadratic programming in python?

Question

In the example given in https://cvxopt.org/examples/tutorial/qp.html, they give the syntax for the situation when there is one equality constraint but I am having problems getting it right when I have three equality constraints.

Do you plan to give us any details about the problems?

Klaus D.
– Klaus D.

2019-11-13 01:27:49 +00:00
Commented Nov 13, 2019 at 1:27 — Klaus D.
– Klaus D., Commented Nov 13, 2019 at 1:27

Dharman · Accepted Answer · 2020-07-13 16:41:04Z

4

CVXOPT can be easily implemented with several inequality as well as equality constraint and they follow the same logic.

So for the inequality Gx <= h you will have

G = opt.matrix(np.concatenate([G1,Gn])
h = opt.matrix(np.concatenate([h1,hn])

The same logic will apply for multiple equality constraint where Ax == b, hence

A = opt.matrix(np.concatenate([A1,An])
b = opt.matrix(np.concatenate([b1,bn])

then you can set the solver to be

sol = solvers.qp(P, q, G, h, A, b)

The only thing you need to be careful not to have error is to make sure you concatenate matrixes, hence you need to transform value into matrix

edited Jul 13, 2020 at 16:41

Dharman♦

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answered Jul 13, 2020 at 16:35

Marco_sbt

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