TITLE: Numerical minimization spectral norm under diagonal similarity
QUESTION [5 upvotes]: This question is a follow up.
Let $A$ be a real square matrix of size $n \times n$. How to determine the minimum spectral norm under diagonal similarity, i.e.,
$$
s(A) = \inf_{D} \lVert D^{-1} A D\rVert_2,
$$ 
where $D$ is a non-singular, diagonal real matrix. As it is unlikely to find an analytical upper bound, I would like to ask how $s(A)$ could be determined numericallly.

REPLY [4 votes]: Here is a better, more direct solution.
This problem can be cast as a Generalized Eigenvalue Problem as is shown by 
Boyd, El Ghaoui, Feron, and Balakrishnan on page 39 (§3.3) of Linear Matrix Inequalities in System and Control Theory:
$$
s(A) = \inf \left\{\gamma \mid A^*PA < \gamma^2 P \textrm{ for diagonal } P > 0
 \right\}
$$

Previous answer
Unfortunately behind a paywall, but following my own comment about chasing literature on matrix balancing, I found the following old paper that solves your problem (EDIT: As noted by Sebastian, this paper actually only provides a solution for a restricted case), not only for the operator norm, but for a variety of other norms.
T. Ström. Minimization of norms and logarithmic norms by diagonal similarities. Computing, March 1972, Volume 10, Issue 1–2, pp 1–7.