How can I maximize the minimum singular value of a matrix in cvx? The minimum singular value is a concave function of the underlying matrix so cvx should be able to handle this. Unfortunately there is no function that can do this.
Unlike the minimum eigenvalue, which is concave, the minimum singular value is the difference of two convex functions, and is neither concave nor convex for matrix dimension >= 2. See section 5 of “On Extreme Singular Values of Matrix Valued Functions”, L. Qi and R.S. Womersley, Journal of Convex Analysis, volume 3 (1996), available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.43.299&rep=rep1&type=pdf . A simple 2 by 2 counter-example to the concavity of the minimum singular value is presented in Remark 5.2.