start:hype_tutorials:automatic_calibration

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start:hype_tutorials:automatic_calibration [2019/01/09 13:14] cpers [Introduction] |
start:hype_tutorials:automatic_calibration [2019/02/25 16:58] (current) cpers [Quasi-Newton methods (task Q1 and Q2)] |
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|Figure 10: Example of optpar.txt file for the quasi-Newton method| | |Figure 10: Example of optpar.txt file for the quasi-Newton method| | ||

- | The quasi-Newton methods optimise all parameters at the same time. The parameter set is optimized with the line search routine starting from the point of the current best parameters. The direction of the search is determined by the gradient of the criteria surface at this point. The gradient can be estimated in three different ways in HYPE, the two quasi-Newton methods described in this section and the one called steepest descent in the next section. The optimization continues until one of several interruption criteria is fulfilled. | + | The quasi-Newton methods optimise all parameters at the same time. The direction of the search is determined by the gradient of the criteria surface at the point of the current best parameters. The parameter set is optimized with the line search routine along the line determined by the gradient. The gradient can be estimated in three different ways in HYPE, the two quasi-Newton methods described in this section and the one called steepest descent in the next section. The optimization continues until one of several interruption criteria is fulfilled. |

Calculating the gradient for the quasi-Newton method involves updating the inverse Hessian matrix. This can be done by two methods, both described in Nocedal and Wright (2006). Task Q1 uses the DFP (Davidon-Fletcher-Powell) method and task Q2 uses the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method. | Calculating the gradient for the quasi-Newton method involves updating the inverse Hessian matrix. This can be done by two methods, both described in Nocedal and Wright (2006). Task Q1 uses the DFP (Davidon-Fletcher-Powell) method and task Q2 uses the BFGS (Broyden-Fletcher-Goldfarb-Shanno) method. |

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