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Efficient index reduction algorithm for large scale systems of differential algebraic equations
Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering. Chinese Academic Science, Peoples R China.
Chinese Academic Science, Peoples R China.
Chinese Academic Science, Peoples R China.
Bielefeld University of Appl Science, Germany.
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2016 (English)In: Applied Mathematics and Computation, ISSN 0096-3003, E-ISSN 1873-5649, Vol. 277, 10-22 p.Article in journal (Refereed) PublishedText
Abstract [en]

In many mathematical models of physical phenomenons and engineering fields, such as electrical circuits or mechanical multibody systems, which generate the differential algebraic equations (DAEs) systems naturally. In general, the feature of DAEs is a sparse large scale system of fully nonlinear and high index. To make use of its sparsity, this paper provides a simple and efficient algorithm for index reduction of large scale DAEs system. We exploit the shortest augmenting path algorithm for finding maximum value transversal (MVT) as well as block triangular forms (BTFs). We also present the extended signature matrix method with the block fixed point iteration and its complexity results. Furthermore, a range of nontrivial problems are demonstrated by our algorithm. (C) 2015 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2016. Vol. 277, 10-22 p.
Keyword [en]
Differential algebraic equations; Sparsity; Shortest augmenting path; Block triangular forms; Structural analysis
National Category
Computer and Information Science
URN: urn:nbn:se:liu:diva-125794DOI: 10.1016/j.amc.2015.11.091ISI: 000369369700002OAI: diva2:910334

Funding Agencies|China 973 Project [NKBRPC-201103302402]; National Natural Science Foundation of China [61402537, 91118001]; Youth Innovation Promotion Association CAS [2012335]; China Postdoctoral Science Foundation [2012M521692]; Open Project of Chongqing Key Laboratory of Automated Reasoning and Cognition [CARC2014004]

Available from: 2016-03-08 Created: 2016-03-04 Last updated: 2016-03-08

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Qin, XiaolinFritzson, Peter
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