# Fast algorithm for maximizing smallest eigenvalue of linear combination of hermitian matrices

I have an engineering back ground. Due to work, I came across this problem \begin{align} &\max_{\lambda,y_i\in \mathbb{R}}~\lambda \\\ s.t.~&\left(\mathbf{A}_0+\sum_{i=1}^{K}y_i\mathbf{A}_i\right)-\lambda\mathbf{I}\geq 0 \end{align} where $\mathbf{A}_i$ are all hermitian matrices. We are seeking $\lambda$ and $y_i$. I know that this is called a Linear Matrix Inequality problem and can be solved by a general convex package (for eg, CVX). To me, it seems like we are looking for a matrix formed from the linear combination of given hermitian matrices whose smallest eigenvalue is as maximum as possible among all such combinations. I was wondering if they are iterative algorithms to solve this problem which are simple to implement. Please point me to relevant references.

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