Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Structural analysis based dummy derivative selection for differential algebraic equations

McKenzie, Ross and Pryce, John D. ORCID: 2017. Structural analysis based dummy derivative selection for differential algebraic equations. BIT Numerical Mathematics 57 (2) , pp. 433-462. 10.1007/s10543-016-0642-9

[thumbnail of 201704McKenziePryce-BIT-paper.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (872kB) | Preview


The signature matrix structural analysis method developed by Pryce provides more structural information than the commonly used Pantelides method and applies to differential-algebraic equations (DAEs) of arbitrary order. It is useful to consider how existing methods using the Pantelides algorithm can benefit from such structural analysis. The dummy derivative method is a technique commonly used to solve DAEs that can benefit from such exploitation of underlying DAE structures and information found in the Signature Matrix method. This paper gives a technique to find structurally necessary dummy derivatives and how to use different block triangular forms effectively when performing the dummy derivative method and then provides a brief complexity analysis of the proposed approach. We finish by outlining an approach that can simplify the task of dummy pivoting.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Uncontrolled Keywords: DAEs Numerical Analysis
Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
Publisher: Springer Verlag
ISSN: 0006-3835
Funders: The Leverhulme Trust
Date of First Compliant Deposit: 30 May 2017
Date of Acceptance: 8 November 2016
Last Modified: 21 Oct 2022 07:53

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics