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arXiv 0808.1502math.PRmath.SP
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随机Markov矩阵的循环律定理

Circular Law Theorem for Random Markov Matrices

Charles Bordenave, Pietro Caputo, Djalil Chafai

摘要:

考虑具有i.i.d.的nxn随机矩阵X。密度、均值m和正方差σ有限的非负项^[2]。设M是具有i.i.d.的n × n随机马尔可夫矩阵。通过将X的每一行除以其总和而从X获得的行。特别地,当X 11服从指数律时,则M属于随机随机阵的Dirichlet Markov包络。我们的主要结果表明,当概率为1时,n^(1/2)M的复谱的计数概率测度在半径为σ/m的中心圆盘上随着n趋于无穷大而弱收敛到一致律。有界密度假设纯粹是技术性的,来自于我们控制预解式的算子范数的方式。

英文摘要:

Consider an nxn random matrix X with i.i.d. nonnegative entries with bounded density, mean m, and finite positive variance sigma^2. Let M be the nxn random Markov matrix with i.i.d. rows obtained from X by dividing each row of X by its sum. In particular, when X11 follows an exponential law, then M belongs to the Dirichlet Markov Ensemble of random stochastic matrices. Our main result states that with probability one, the counting probability measure of the complex spectrum of n^(1/2)M converges weakly as n tends to infinity to the uniform law on the centered disk of radius sigma/m. The bounded density assumption is purely technical and comes from the way we control the operator norm of the resolvent.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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