| Optimisation tutorial 1 | IPOPT NLP solver, its setup and options. |
| Optimisation tutorial 2 | Bonmin MINLP solver, its setup and options. |
| Optimisation tutorial 3 | NLOPT NLP solvers, their setup and options. |
| Optimisation tutorial 4 | Interoperability between DAE Tools and 3rd party optimisation software (scipy.optimize) used to minimise the Rosenbrock function. |
| Optimisation tutorial 5 | Interoperability between DAE Tools and 3rd party optimisation software (scipy.optimize) used to fit the simple function with experimental data. |
| Optimisation tutorial 6 | daeMinpackLeastSq solver. |
| Optimisation tutorial 7 | Monitoring the optimisation progress (pyQt GUI). |
This tutorial introduces IPOPT NLP solver, its setup and options.
Files
| Model report | opt_tutorial1.xml |
| Runtime model report | opt_tutorial1-rt.xml |
| Source code | opt_tutorial1.py |
This tutorial introduces Bonmin MINLP solver, its setup and options.
Files
| Model report | opt_tutorial2.xml |
| Runtime model report | opt_tutorial2-rt.xml |
| Source code | opt_tutorial2.py |
This tutorial introduces NLOPT NLP solver, its setup and options.
Files
| Model report | opt_tutorial3.xml |
| Runtime model report | opt_tutorial3-rt.xml |
| Source code | opt_tutorial3.py |
This tutorial shows the interoperability between DAE Tools and 3rd party optimization software (scipy.optimize) used to minimize the Rosenbrock function.
DAE Tools simulation is used to calculate the objective function and its gradients, while scipy.optimize.fmin function (Nelder-Mead Simplex algorithm) to find the minimum of the Rosenbrock function.
Files
| Model report | opt_tutorial4.xml |
| Runtime model report | opt_tutorial4-rt.xml |
| Source code | opt_tutorial4.py |
This tutorial shows the interoperability between DAE Tools and 3rd party optimization software (scipy.optimize) used to fit the simple function with experimental data.
DAE Tools simulation object is used to calculate the objective function and its gradients, while scipy.optimize.leastsq function (a wrapper around MINPACK’s lmdif and lmder) implementing Levenberg-Marquardt algorithm is used to estimate the parameters.
Files
| Model report | opt_tutorial5.xml |
| Runtime model report | opt_tutorial5-rt.xml |
| Source code | opt_tutorial5.py |
daeMinpackLeastSq module test.
Files
| Model report | opt_tutorial6.xml |
| Runtime model report | opt_tutorial6-rt.xml |
| Source code | opt_tutorial6.py |
This tutorial introduces monitoring optimization progress.
Files
| Model report | opt_tutorial7.xml |
| Runtime model report | opt_tutorial7-rt.xml |
| Source code | opt_tutorial7.py |
