Results 21 to 30 of about 2,240,663 (294)

A fast algorithm for matrix balancing [PDF]

open access: yes, 2013
As long as a square nonnegative matrix A contains sufficient nonzero elements, then the matrix can be balanced, that is we can find a diagonal scaling of A that is doubly stochastic. A number of algorithms have been proposed to achieve the balancing, the
Knight, Philip, Ruiz, Daniel
core   +2 more sources

A posteriori error estimation for stochastic static problems [PDF]

open access: yes, 2014
To solve stochastic static field problems, a discretization by the Finite Element Method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes for example) being random variables. To solve this stochastic system, the
MAC, Hung, CLENET, Stephane
core   +1 more source

Inclusion regions and bounds for the eigenvalues of matrices with a known eigenpair

open access: yesSpecial Matrices, 2020
Let (λ, v) be a known real eigenpair of an n×n real matrix A. In this paper it is shown how to locate the other eigenvalues of A in terms of the components of v. The obtained region is a union of Gershgorin discs of the second type recently introduced by
Marsli Rachid, Hall Frank J.
doaj   +1 more source

The Observable Representation

open access: yesEntropy, 2019
The observable representation (OR) is an embedding of the space on which a stochastic dynamics is taking place into a low dimensional Euclidean space. The most significant feature of the OR is that it respects the dynamics.
L. S. Schulman
doaj   +1 more source

A Geršgorin-type eigenvalue localization set with n parameters for stochastic matrices

open access: yesOpen Mathematics, 2018
A set in the complex plane which involves n parameters in [0, 1] is given to localize all eigenvalues different from 1 for stochastic matrices. As an application of this set, an upper bound for the moduli of the subdominant eigenvalues of a stochastic ...
Wang Xiaoxiao, Li Chaoqian, Li Yaotang
doaj   +1 more source

Controlling Stochastic Sensitivity by Feedback Regulators in Nonlinear Dynamical Systems with Incomplete Information

open access: yesMathematics, 2021
The problem of synthesis of stochastic sensitivity for equilibrium modes in nonlinear randomly forced dynamical systems with incomplete information is considered.
Irina Bashkirtseva
doaj   +1 more source

Best approximation of ( G 1 , G 2 ) $(\mathcal{G}_{1},\mathcal{G}_{2})$ -random operator inequality in matrix Menger Banach algebras with application of stochastic Mittag-Leffler and H $\mathbb{H}$ -Fox control functions

open access: yesJournal of Inequalities and Applications, 2022
We stabilize pseudostochastic ( G 1 , G 2 ) $(\mathcal{G}_{1},\mathcal{G}_{2})$ -random operator inequality using a class of stochastic matrix control functions in matrix Menger Banach algebras.
Safoura Rezaei Aderyani   +3 more
doaj   +1 more source

Fundamental Structure of General Stochastic Dynamical Systems: High-Dimension Case

open access: yesJournal of Mathematics, 2022
No one has proved that mathematically general stochastic dynamical systems have a special structure. Thus, we introduce a structure of a general stochastic dynamical system. According to scientific understanding, we assert that its deterministic part can
Haoyu Wang   +3 more
doaj   +1 more source

Ordering Properties of the Smallest and Largest Order Statistics from Exponentiated Location-Scale Models Under Random Shocks

open access: yesRevstat Statistical Journal, 2023
In this paper, we discuss stochastic comparisons of lifetimes of series and parallel systems when the components are exponentiated location-scale models under random shocks. The results established here are developed in two directions.
Molod Abdolahi   +2 more
doaj   +1 more source

Phylogenetic Stochastic Mapping Without Matrix Exponentiation [PDF]

open access: yesJournal of Computational Biology, 2014
Abstract Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organism carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov chain (CTMC) and works well for small state spaces. The computations slow
Jan Irvahn, Vladimir N. Minin
openaire   +6 more sources

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