Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology

Revision as of 14:16, 25 February 2020 by Bwday (talk | contribs) (Summary)

1 Citation

Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology, Y Schälte, P Stapor, J Hasenauer, IFAC-PapersOnLine 51 (19), 98-101.

2 Summary

Different parameter estimation settings necessitate the use of different optimization techniques. Hence, different local and global optimization techniques were compared in this paper by use of classic optimization test problems and also 8 ODE models. This article especially focused on the performance of derivative free optimizations (DFO) and if they are a valuable alternative to gradient-based methods.

3 Study outcomes

List the paper results concerning method comparison and benchmarking:

3.1 Outcome O1

The performance of ...

Outcome O1 is presented as Figure X in the original publication.

3.2 Outcome O2

...

Outcome O2 is presented as Figure X in the original publication.

3.3 Outcome On

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Outcome On is presented as Figure X in the original publication.

3.4 Further outcomes

If intended, you can add further outcomes here.


4 Study design and evidence level

4.1 General aspects

You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:

4.2 Design for Outcome O1

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

4.3 Design for Outcome O2

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

...

4.4 Design for Outcome O

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
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5 Further comments and aspects

6 References

The list of cited or related literature is placed here.