A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps

In order to solve continuous stochastic Lyapunov equations, a novel implicit iterative algorithm is presented by means of successive over relaxation (SOR) iteration in this article. Throughout this method, three tuning parameters are added for the improvement of the convergence rate. It is shown tha...

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Main Authors: Tianrui Wu, Peiqi Huang, Hong Chen
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/12/7/1080
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author Tianrui Wu
Peiqi Huang
Hong Chen
author_facet Tianrui Wu
Peiqi Huang
Hong Chen
author_sort Tianrui Wu
collection DOAJ
description In order to solve continuous stochastic Lyapunov equations, a novel implicit iterative algorithm is presented by means of successive over relaxation (SOR) iteration in this article. Throughout this method, three tuning parameters are added for the improvement of the convergence rate. It is shown that this algorithm is monotonically bounded, and the convergence condition is also given and extended. Applying the latest updated estimates, this algorithm can attain a better convergence performance compared with other existing iterative algorithms when choosing appropriate tuning parameters. Finally, a numerical example is provided to illustrate the feasibility and priority of this approach.
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spelling doaj.art-eb82611306ce4d8b818216d9b7df393f2024-04-12T13:22:49ZengMDPI AGMathematics2227-73902024-04-01127108010.3390/math12071080A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov JumpsTianrui Wu0Peiqi Huang1Hong Chen2School of Science, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Science, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Science, Nanjing Forestry University, Nanjing 210037, ChinaIn order to solve continuous stochastic Lyapunov equations, a novel implicit iterative algorithm is presented by means of successive over relaxation (SOR) iteration in this article. Throughout this method, three tuning parameters are added for the improvement of the convergence rate. It is shown that this algorithm is monotonically bounded, and the convergence condition is also given and extended. Applying the latest updated estimates, this algorithm can attain a better convergence performance compared with other existing iterative algorithms when choosing appropriate tuning parameters. Finally, a numerical example is provided to illustrate the feasibility and priority of this approach.https://www.mdpi.com/2227-7390/12/7/1080iterative algorithmsSORcontinuous stochastic Lyapunov equationsMarkov jumps
spellingShingle Tianrui Wu
Peiqi Huang
Hong Chen
A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps
Mathematics
iterative algorithms
SOR
continuous stochastic Lyapunov equations
Markov jumps
title A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps
title_full A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps
title_fullStr A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps
title_full_unstemmed A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps
title_short A Successive over Relaxation Implicit Iterative Algorithm for Solving Stochastic Linear Systems with Markov Jumps
title_sort successive over relaxation implicit iterative algorithm for solving stochastic linear systems with markov jumps
topic iterative algorithms
SOR
continuous stochastic Lyapunov equations
Markov jumps
url https://www.mdpi.com/2227-7390/12/7/1080
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