Results 211 to 220 of about 1,819 (238)

Parallel computing of eigenvalue of doubly stochastic matrix

Fifth International Conference on Algorithms and Architectures for Parallel Processing, 2002. Proceedings., 2003
The transition probability matrix of the Markov cipher is doubly stochastic. The eigenvalue of the matrix with maximum magnitude less than one plays an important role in designing the Markov cipher. This paper provides a parallel algorithm for computing the eigenvalue of the doubly stochastic matrix A of size 65535/spl times/65535, which comes from a ...
null He Dake, null Wang Jianbo
openaire   +1 more source

On the latent roots of a doubly stochastic matrix

Journal of the Australian Mathematical Society, 1973
Under certain general conditions an n x n stochastic matrix P = (pij), whereis known to possess the “ergodic” property.
openaire   +2 more sources

An expansion for the permanent of a doubly stochastic matrix

Journal of the Australian Mathematical Society, 1973
The permanent of an n-square matrix A = (aij) is defined by where Sn is the symmetric group of order n. Kn will denote the convex set of all n-square doubly stochastic matrices and K0n its interior. Jn ∈ Kn will be the matrix with all elements equal to 1/n. If M ∈ K0n, then M lies on a line segment passing through Jn and another B ∈ Kn — K0n.
openaire   +2 more sources

Doubly stochastic and permutation solutions to AXA = XAX when A is a permutation matrix

Linear Algebra and its Applications, 2023
Assume that \(A\in\mathbb{C}^{n\times n}\) (or \(A \in \mathbb{R}^{n\times n}\)) is a nonzero square matrix. A Yang-Baxter-like matrix equation is \[ AXA=XAX , \] which has two obvious solutions, namely \(X=0\) and \(X=A\). The author finds a sufficient and necessary condition for the solvability of the above matrix equation in the set of doubly ...
openaire   +1 more source

A Jacobian-Free Method for the Nearest Doubly Stochastic Matrix Problem

Journal of Optimization Theory and Applications
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianghua Yin, Yaobiao Li, Chunming Tang 0002   +2 more
openaire   +1 more source

Convergence rate of a distributed algorithm for matrix scaling to doubly stochastic form

53rd IEEE Conference on Decision and Control, 2014
Motivated by matrix scaling applications and, more recently, distributed averaging previous work has considered settings where the interconnections between components in a distributed system are captured by a strongly connected directed graph (digraph) and each component aims to assign assigning weights on its outgoing edges (based on the weights on ...
Domínguez-Garcia, A. D., Hadjicostis, Christoforos N., Domínguez-Garcia, A. D., Hadjicostis, Christoforos N.   +3 more
openaire   +2 more sources

Concerning the Magnitude of the Entries in a Doubly Stochastic Matrix

Linear and Multilinear Algebra, 1981
We show that for every nxn doubly stochastic matrix A, for every entry aij , where is the Euclidean norm. The cases for which equality holds are discussed in detail.
openaire   +1 more source

When does a digraph admit a doubly stochastic adjacency matrix?

Proceedings of the 2010 American Control Conference, 2010
Digraphs with doubly stochastic adjacency matrices play an essential role in a variety of cooperative control problems including distributed averaging, optimization, and gossiping. In this paper, we fully characterize the class of digraphs that admit an edge weight assignment that makes the digraph adjacency matrix doubly stochastic. As a by-product of
Bahman Gharesifard, Jorge Cortés 0001
openaire   +1 more source

On the Use of the Doubly Stochastic Matrix Models for the Quadratic Assignment Problem

Evolutionary Computation
Abstract Permutation problems have captured the attention of the combinatorial optimization community for decades due to the challenge they pose. Although their solutions are naturally encoded as permutations, in each problem, the information to be used to optimize them can vary substantially.
Valentino Santucci, Josu Ceberio
openaire   +2 more sources

Band selection of hyperspectral data with low-rank doubly stochastic matrix decomposition

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
In this article, a clustering-based band selection method is proposed to tackle the dimension reduction problem of hyperspectral data. The method is essentially based on low-rank doubly stochastic matrix decomposition, which is more stable than current low-rank approximation clustering methods.
openaire   +1 more source