Analysis reveals that optimization enhances inner bounds for extreme eigenvalues in real symmetric matrices, suggesting efficient applications.
Here, inner bounds for the extreme eigenvalues of a real symmetric matrix are derived by using a two-dimensional subspace of Rn. The addition of a single parameter to the basis vectors and optimization thereof results in superior bounds, as compared to using fixed basis vectors. This is a rather simple procedure that is easily automated.
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