Distribution of Eigenvalues of Highly Palindromic Toeplitz Matrices

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

36 pages, 7 images, second draft. To appear in the Journal of Theoretical Probability

Scientific paper

Consider the ensemble of real symmetric Toeplitz matrices whose entries are i.i.d random variables chosen from a fixed probability distribution p of mean 0, variance 1 and finite higher moments. Previous work [BDJ,HM] showed that the limiting spectral measures (the density of normalized eigenvalues) converge weakly and almost surely to a universal distribution almost that of the Gaussian, independent of p. The deficit from the Gaussian distribution is due to obstructions to solutions of Diophantine equations and can be removed (see [MMS]) by making the first row palindromic. In this paper, we study the case where there is more than one palindrome in the first row of a real symmetric Toeplitz matrix. Using the method of moments and an analysis of the resulting Diophantine equations, we show that the moments of this ensemble converge to an universal distribution with a fatter tail than any previously seen limiting spectral measure.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Distribution of Eigenvalues of Highly Palindromic Toeplitz Matrices does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Distribution of Eigenvalues of Highly Palindromic Toeplitz Matrices, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distribution of Eigenvalues of Highly Palindromic Toeplitz Matrices will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-341129

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.