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dc.contributor.authorToivanen, Jukka
dc.contributor.authorMäkinen, Raino
dc.date.accessioned2011-10-05T13:19:47Z
dc.date.available2012-09-22T21:45:03Z
dc.date.issued2011
dc.identifier.citationToivanen, J., & Mäkinen, R. (2011). Implementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization. <i>Optimization Methods and Software</i>, <i>26</i>(4-5), 601-616. <a href="https://doi.org/10.1080/10556781003642305" target="_blank">https://doi.org/10.1080/10556781003642305</a>
dc.identifier.otherCONVID_20751341
dc.identifier.otherTUTKAID_47101
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/36760
dc.description.abstractIn 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.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofseriesOptimization Methods and Software
dc.subject.otherautomaattinen derivointi
dc.subject.othermuotoherkkyysanalyysi
dc.subject.othermuodon optimointi
dc.subject.otherautomatic differentiation
dc.subject.othershape sensitivity analysis
dc.subject.othershape optimization
dc.titleImplementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-2011092711454
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2011-09-27T06:43:05Z
dc.contributor.publisherTaylor & Francis
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange601-616
dc.relation.issn1055-6788
dc.relation.numberinseries4-5
dc.relation.volume26
dc.type.versionacceptedVersion
dc.rights.copyright© 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.rights.accesslevelopenAccessfi
dc.relation.doi10.1080/10556781003642305


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