6533b835fe1ef96bd129f739
RESEARCH PRODUCT
Circular law for sparse random regular digraphs
Konstantin TikhomirovNicole Tomczak-jaegermannAlexander E. LitvakAnna LytovaPierre Youssefsubject
General Mathematicsregular graphsrandom matrices01 natural sciencesCombinatoricsMatrix (mathematics)FOS: Mathematics60B20 15B52 46B06 05C80Adjacency matrix0101 mathematicsrandom graphsMathematicsRandom graphlogarithmic potentialWeak convergenceDegree (graph theory)sparse matricesApplied MathematicsProbability (math.PR)010102 general mathematicsCircular lawSingular valueCircular lawintermediate singular valuesRandom matrixMathematics - Probabilitydescription
Fix a constant $C\geq 1$ and let $d=d(n)$ satisfy $d\leq \ln^{C} n$ for every large integer $n$. Denote by $A_n$ the adjacency matrix of a uniform random directed $d$-regular graph on $n$ vertices. We show that, as long as $d\to\infty$ with $n$, the empirical spectral distribution of appropriately rescaled matrix $A_n$ converges weakly in probability to the circular law. This result, together with an earlier work of Cook, completely settles the problem of weak convergence of the empirical distribution in directed $d$-regular setting with the degree tending to infinity. As a crucial element of our proof, we develop a technique of bounding intermediate singular values of $A_n$ based on studying random normals to rowspaces and on constructing a product structure to deal with the lack of independence between the matrix entries.
year | journal | country | edition | language |
---|---|---|---|---|
2020-10-29 | Journal of the European Mathematical Society |