# Optimizing directly on the eigenspectrum of a matrix

I have an application where I want to the eigenvalues of the graph to be involved in the objective and constraints in a flexible way (moreso than just the nuclear or frobenius norm). Whats a good survey or intro source to this sort of optimization?

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Naturally, the answer very much depends on the function you'd like to optimize. I recommend looking at:

1. Proposition 4.2.1 in Lectures on Modern Convex Optimization by Ben-Tal and Nemirovski. It describes a large set of eigenvalue optimization problems which can be written as semidefinite programs. Specifically, if $g(x_1,\ldots,x_n)$ is a symmetric function such the set $t \geq g(x_1,\ldots,x_n)$ has a semidefinite representation, then so does the set $t \geq g(\lambda(X))$, where $\lambda(X)$ is a vector of eigenvalues of a symmetric matrix $X$.

2. Section 4.2 in the same book, which gives some other examples of functions of eigenvalues that can be written in this way (for example, sums of $k$ largest eigenvalues of a symmetric matrix).

3. On the other hand, these types of problems can quickly become NP-hard. The paper Maximum algebraic connectivity augmentation is NP-hard by Damon Mosk-Aoyama shows that the problem of adding a prespecified number of edges to the graph to maximize the second-smallest eigenvalue of the Laplacian is NP-hard.

4. The papers Eigenvalue Optimization by Lewis and Overton and The Mathematics of Eigenvalue Optimization by Lewis.

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Many (but not all) problems involving the eigenvalues of a graph are convex optimization problems that can be formulated as semidefinite programming problems. There are a number of "tricks" that you need to learn in order to formulate problems as SDP's. Once you've got an SDP, there are a number of software packages that can be used to solve the SDP.

You should check out the SIAM Review paper on semidefinite programming by Vandenberghe and Boyd:

L. Vandenberghe and S. Boyd. Semidefinite Programming. SIAM Review, 38(1): 49-95, March 1996.

http://stanford.edu/~boyd/papers/sdp.html

Vandenberghe and Boyd also have a textbook on convex optimization- you can read the .pdf online for free. See

http://www.stanford.edu/~boyd/cvxbook/

Unfortunately, there are lots of eigenvalue optimization problems that cannot be formulated as convex optimization problems. These are much harder (if not practically impossible) to solve.

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