From the Littlewood-Offord problem to the Circular Law: universality of the spectral distribution of random matrices

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

25 pages, 8 figures, to appear, Bull. Amer. Math. Soc. Various corrections and referee suggestions incorporated

Scientific paper

The famous \emph{circular law} asserts that if $M_n$ is an $n \times n$ matrix with iid complex entries of mean zero and unit variance, then the empirical spectral distribution (ESD) of the normalized matrix $\frac{1}{\sqrt{n}} M_n$ converges almost surely to the uniform distribution on the unit disk $\{z \in \C: |z| \leq 1 \}$. After a long sequence of partial results that verified this law under additional assumptions on the distribution of the entries, the full circular law was recently established in \cite{TVcir2}. In this survey we describe some of the key ingredients used in the establishment of the circular law, in particular recent advances in understanding the Littlewood-Offord problem and its inverse.

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

From the Littlewood-Offord problem to the Circular Law: universality of the spectral distribution of random 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 From the Littlewood-Offord problem to the Circular Law: universality of the spectral distribution of random matrices, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and From the Littlewood-Offord problem to the Circular Law: universality of the spectral distribution of random matrices will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-368503

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