In order to estimate the eigenvalues of a real symmetric $n\times n$ matrix, I intend to use the Gershgorin Circle Theorem. Unfortunately, the examples one might find on the internet are a bit confusing; What would be the mathematicalformula for deriving the eigenvalue estimates?I understand that certain disks are formed, each centered at the diagonal entry, with the radius equal to the summation of absolute values of the associated off-diagonal row entries. (The example fromhttp://en.wikipedia.org/wiki/Gershgorin_circle_theoremis clear with the disks, but not with the eigenvalue estimation)Which steps to take from this point to get the estimates on the eigenvalues?From Theorem 2.1 inhttp://buzzard.ups.edu/courses/2007spring/projects/brakkenthal-paper.pdfone could understand the eigenvalue $ranges$, but the example 2.3 from the abovepaper gives concrete eigenvalue estimates (some of which are negative). I would appreciate if someone explains this. I'm interested in the largest (positive) and smallest (possibly negative) eigenvalue estimates.
Gershgorin Circle Theorem to estimate the eigenvalues
linear algebra;matrices;eigensystem
In gerschgorin's theorem, the diagonal entries $A_{ii}$ of the matrix are the eigenvalue estimates, and the radii $r_i$ of the Gerschgorin disks are corresponding error bounds. Thus $\min_i A_{ii}-r_i$ is a lower bound on the eigenvalues, and $\max_i A_{ii}+r_i$ is an upper bound.Note that these bounds are generally poor unless the off-diagonal entries are tiny. in the latter case, one can get excellent bounds by an appropriate prior similarity transform of the matrix with as suitable scaling matrix.To get good bounds for a general matrix, one must compute an approximate eigensystem and then express the matrix in this basis by a simiarity transform. This doesn't change the eigenvalues but makes the off-diagonal entries small, so that the above applies.