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Current Trends in High Performance Computing and Its Applications pp 339–344Cite as

Derivative Based vs. Derivative Free Optimization Methods for Nonlinear Optimum Experimental Design

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References

  1. I. Bauer et al., Numerical Methods for Initial Value Problems and Derivative Generation for DAE Models with Application to Optimum Experimental Design of Chemical Processes, Scientific Computing in Chemical Engineering II, F. Keil, et al., eds., Springer-Verlag, Heidelberg, 2 (1999), 282–289.

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Author information

Authors and Affiliations

  1. Interdisciplinary Center for Scientific Computing, University of Heidelberg, Im Neuenheimer Feld 368, D-69120, Heidelberg, Germany

    Stefan Körkel & Sebastian Sager

  2. Intelligent Information Processing Laboratory, Fudan University, Shanghai, China

    Huiqin Qu

  3. Deutsche Börse AG, 60485, Frankfurt am Main, Germany

    Gerd Rücker

Authors
  1. Stefan Körkel
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  2. Huiqin Qu
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  3. Gerd Rücker
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  4. Sebastian Sager
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Editor information

Editors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 200072, Shanghai, People’s Republic of China

    Wu Zhang  & Weiqin Tong  & 

  2. Department of Mathematics, Southern Methodist University, 75275-0156, Dallas, TX, USA

    Zhangxin Chen

  3. Department of Mathematics, University of Houston, 77204-3476, Houston, TX, USA

    Roland Glowinski

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© 2005 Springer-Verlag Berlin Heidelberg

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Körkel, S., Qu, H., Rücker, G., Sager, S. (2005). Derivative Based vs. Derivative Free Optimization Methods for Nonlinear Optimum Experimental Design. In: Zhang, W., Tong, W., Chen, Z., Glowinski, R. (eds) Current Trends in High Performance Computing and Its Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27912-1_41

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