Implementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization

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dc.contributor.author Toivanen, Jukka
dc.contributor.author Mäkinen, Raino
dc.date.accessioned 2011-10-05T13:19:47Z
dc.date.available 2012-09-22T21:45:03Z
dc.date.issued 2011
dc.identifier.citation Toivanen, J. & Mäkinen, R. (2011). Implementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization. Optimization Methods and Software, 26 (4-5), 601-616. doi:10.1080/10556781003642305
dc.identifier.issn 1055-6788
dc.identifier.other TUTKAID_47101
dc.identifier.uri http://hdl.handle.net/123456789/36760
dc.description.abstract In this paper, we present the details of a simple lightweight implementation of the so-called sparse forward mode automatic differentiation (AD) in the C++programming language. Our implementation and the well-known ADOL-C tool (which utilizes taping and compression techniques) are used to compute Jacobian matrices of two nonlinear systems of equations from the MINPACK-2 test problem collection. Timings of the computations are presented and discussed. Moreover, we perform the shape sensitivity analysis of a time-harmonic Maxwell equation solver using our implementation and the tapeless mode of ADOL-C, which implements the dense forward mode AD. It is shown that the use of the sparse forward mode can save computation time even though the total number of independent variables in this example is quite small. Finally, numerical solution of an electromagnetic shape optimization problem is presented. en
dc.language.iso eng
dc.relation.ispartof Optimization Methods and Software
dc.rights © Taylor & Francis. This is an electronic final draft version of an article whose final and defenitive form is published in the print edition of Optimization Methods and Software which is available online at: http://www.tandfonline.com.
dc.subject.other automaattinen derivointi fi
dc.subject.other muotoherkkyysanalyysi fi
dc.subject.other muodon optimointi fi
dc.subject.other automatic differentiation en
dc.subject.other shape sensitivity analysis en
dc.subject.other shape optimization en
dc.title Implementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization
dc.type Article en
dc.identifier.urn URN:NBN:fi:jyu-2011092711454
dc.subject.kota 113
dc.contributor.laitos Tietotekniikan laitos fi
dc.contributor.laitos Mathematical Information Technology en
dc.contributor.oppiaine tietotekniikka fi
jyx.tutka.volyme 26
jyx.tutka.mnumber 4-5
jyx.tutka.pagetopage 601-616
dc.type.uri http://purl.org/eprint/type/JournalArticle
dc.identifier.doi 10.1080/10556781003642305
dc.date.updated 2011-09-27T06:43:05Z
dc.description.version Final draft version
dc.contributor.publisher Taylor & Francis
eprint.status http://purl.org/eprint/type/status/PeerReviewed

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