# An optimization method for bounding the eigenvalues of a unknown non symmetric matrix

Given a positive objective function $$f$$ that acts on a real-valued matrix $$A$$, I am interested in the following problem

$$\underset{A \in \mathbb{R}^{n \times n}}{\text{minimize}} \quad f(A) \quad \text{subject to} \quad b \leq \text{Re}(\lambda_j) \leq a, \quad j=1,2,...n \ ,$$

where $$\lambda_j \in \mathbb{C}$$ are the eigenvalues of $$A$$, and $$\text{Re}(\cdot)$$ is the real part of a complex number. What optimization methods are available for approaching this task? I know there are several techniques for bounding the eigenvalues of symmetric matrices, but I wonder what happens in the non symmetric case (my $$A$$ is not necessarily symmetric)?

• This question was posted originally in the math forum.
• Can you say anything about $A$? Aug 9, 2019 at 16:03
• Unfortunately no, @Richard. $A$ does not have any particular structure. Aug 9, 2019 at 16:50
• Can you provide how $f$ looks like? Aug 9, 2019 at 23:09
• Yes. $f$ could be as simple as $|AX-Y|_F^2$ where $X, Y$ are fixed known matrices and $|\cdot|_F$ is the Frobenius norm. Aug 10, 2019 at 3:01
• Out of curiosity, how big is $n$ generally or what is an upper bound for $n$? Further, how good does the result need to be? Can you do an approximation if it’s significantly faster to compute or do you really need to try and get the global optimum for a potentially general matrix $A$ (meaning no assumed structure)? Aug 10, 2019 at 5:16

$$\begin{array}{rcl} \min\limits_{A \in \mathbb{R}^{n \times n}, P\in \mathbb{R}^{n\times n}} &&f(A)\\ \text{st} && b I \preceq PA + A^TP \preceq a I\\ && P \succ 0 \end{array}$$
Essentially, this is the Lyapunov stability condition. Of course, this problem is a pain to solve because the $$PA$$ term is quadratic, so we effectively have a nonlinear semidefinite program.