Matrices with maximum exponents in the class of doubly stochastic primitive matrices

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Matrices with Maximum Upper Multiexponents in the Class of Primitive, Nearly Reducible Matrices

B. Liu has recently obtained the maximum value for the kth upper multiexponents of primitive, nearly reducible matrices of order n with 1 ≤ k ≤ n. In this paper primitive, nearly reducible matrices whose kth upper multiexponents attain the maximum value are completely characterized.

متن کامل

Fully Indecomposable Exponents of Primitive Matrices

If A is a primitive matrix, then there is a smallest power of A (its fully indecomposable exponent) that is fully indecomposable, and a smallest power of A (its strict fully indecomposable exponent) starting from which all powers are fully indecomposable. We obtain bounds on these two exponents.

متن کامل

Doubly stochastic matrices of trees

In this paper, we obtain sharp upper and lower bounds for the smallest entries of doubly stochastic matrices of trees and characterize all extreme graphs which attain the bounds. We also present a counterexample to Merris’ conjecture on relations between the smallest entry of the doubly stochastic matrix and the algebraic connectivity of a graph in [R. Merris, Doubly stochastic graph matrices I...

متن کامل

Random doubly stochastic tridiagonal matrices

Let Tn be the compact convex set of tridiagonal doubly stochastic matrices. These arise naturally in probability problems as birth and death chains with a uniform stationary distribution. We study ‘typical’ matrices T ∈ Tn chosen uniformly at random in the set Tn. A simple algorithm is presented to allow direct sampling from the uniform distribution on Tn. Using this algorithm, the elements abo...

متن کامل

Conditional Autoregressions with Doubly Stochastic Weight Matrices

A conditional spatial autoregression (CAR) specifies dependence via a weight matrix. Employing a doubly stochastic weight matrix allows users to interpret the CAR prediction rule as a semiparametric prediction rule and as BLUP with smoothing in addition to other benefits. We examine standard and doubly stochastic weight matrices in the context of an illustrative data set to demonstrate feasibil...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Discrete Applied Mathematics

سال: 1999

ISSN: 0166-218X

DOI: 10.1016/s0166-218x(98)00097-3