An Improved KM Algorithm for Computing the Structural Index of DAE System

Modeling and simulation technology is widely used to design complex products in industry. The problem of solving DAEs(Differential Algebraic Equations) is a key part of modeling and simulation technology, and computing the structural index of DAEs correctly and efficiently is very important to solve...

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Main Authors: Yan Zeng, Xuesong Wu, Jianwen Cao
Format: Article
Language:English
Published: SAGE Publishing 2015-09-01
Series:Journal of Algorithms & Computational Technology
Online Access:https://doi.org/10.1260/1748-3018.9.3.233
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spelling doaj-d15e873dad30424d92c78b2bf9e910f82020-11-25T02:54:29ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262015-09-01910.1260/1748-3018.9.3.233An Improved KM Algorithm for Computing the Structural Index of DAE SystemYan Zeng0Xuesong Wu1Jianwen Cao2 State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, ChinaModeling and simulation technology is widely used to design complex products in industry. The problem of solving DAEs(Differential Algebraic Equations) is a key part of modeling and simulation technology, and computing the structural index of DAEs correctly and efficiently is very important to solve DAEs. The traditional algebraic method to compute the structural index is very costly. In this paper, we firstly convert the problem of computing the structural index of DAEs into the maximum weighted matching problem of bipartite graph, reducing a mass of symbolic manipulations; and then, we present an improved KM algorithm(called as Greedy_KM in this paper) based on the properties of DAEs to solve this matching problem. In order to solve the matching problem efficiently, it firstly computes matches as much as possible using greedy strategy, and then call KM algorithm to search the matches for the unmatched vertices after the step of greedy strategy. This paper also gives a set of numerical experiments to evaluate the time performance of our method. The results show that the time performance of Greedy_KM algorithm is significantly improved compared with the traditional Gaussian elimination algorithm and classical KM algorithm.https://doi.org/10.1260/1748-3018.9.3.233
collection DOAJ
language English
format Article
sources DOAJ
author Yan Zeng
Xuesong Wu
Jianwen Cao
spellingShingle Yan Zeng
Xuesong Wu
Jianwen Cao
An Improved KM Algorithm for Computing the Structural Index of DAE System
Journal of Algorithms & Computational Technology
author_facet Yan Zeng
Xuesong Wu
Jianwen Cao
author_sort Yan Zeng
title An Improved KM Algorithm for Computing the Structural Index of DAE System
title_short An Improved KM Algorithm for Computing the Structural Index of DAE System
title_full An Improved KM Algorithm for Computing the Structural Index of DAE System
title_fullStr An Improved KM Algorithm for Computing the Structural Index of DAE System
title_full_unstemmed An Improved KM Algorithm for Computing the Structural Index of DAE System
title_sort improved km algorithm for computing the structural index of dae system
publisher SAGE Publishing
series Journal of Algorithms & Computational Technology
issn 1748-3018
1748-3026
publishDate 2015-09-01
description Modeling and simulation technology is widely used to design complex products in industry. The problem of solving DAEs(Differential Algebraic Equations) is a key part of modeling and simulation technology, and computing the structural index of DAEs correctly and efficiently is very important to solve DAEs. The traditional algebraic method to compute the structural index is very costly. In this paper, we firstly convert the problem of computing the structural index of DAEs into the maximum weighted matching problem of bipartite graph, reducing a mass of symbolic manipulations; and then, we present an improved KM algorithm(called as Greedy_KM in this paper) based on the properties of DAEs to solve this matching problem. In order to solve the matching problem efficiently, it firstly computes matches as much as possible using greedy strategy, and then call KM algorithm to search the matches for the unmatched vertices after the step of greedy strategy. This paper also gives a set of numerical experiments to evaluate the time performance of our method. The results show that the time performance of Greedy_KM algorithm is significantly improved compared with the traditional Gaussian elimination algorithm and classical KM algorithm.
url https://doi.org/10.1260/1748-3018.9.3.233
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