Hybrid optimization method with general switching strategy for parameter estimation

1 Citation

Eva Balsa-Canto, Martin Peifer, Julio R Banga, Jens Timmer and Christian Fleck Hybrid optimization method with general switching strategy for parameter estimation, 2008, BMC Systems Biology, 2:26.

2 Summary

Parameter estimation in systems biology model requires finding the global optimum of the objective function. This is usually done in terms of deterministic local optimizers which run the risk of precisely finding only a local optimum or by stochastic global optimizers which find the global optimum, but do not fully converge. Hence, the authors proposed a hybrid optimization which switches from stochastic global optimization to a local multiple shooting approach with a switching point which is determined dynamically. The hybriod method was demonstrated to vastly outperform stochastic global optimization and single as well as multiple shooting local methods in terms of computation time.

3 Study outcomes

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3.1 Outcome O1

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3.2 Outcome O2

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3.3 Outcome On

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3.4 Further outcomes

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4 Study design and evidence level

4.1 General aspects

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4.2 Design for Outcome O1

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4.3 Design for Outcome O2

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4.4 Design for Outcome O

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6 References

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