From: Guidelines for benchmarking of optimization-based approaches for fitting mathematical models

Abbreviation | Covariate | Typical possible choices |
---|---|---|

C1 | Application problem | Model equations and the data set(s) |

C2 | Primary performance criteria | Convergence per computation time, iteration steps |

C3 | Secondary performance criteria | Documentation, user-friendliness, code quality |

C4 | Parameter scale | Linear vs. log scale |

C5 | Global search strategy | Multiple initial guesses, scatter search algorithms, stochastic search |

C6 | Initial guess strategy | Fixed vs. random, normally distributed vs. uniform vs. latin-hypercube |

C7 | Parameter constraints | Upper and lower bounds |

C8 | Prior knowledge | None vs. (weakly) informative priors |

C9 | ODE integration implementation | SUNDIALS, Matlab, R |

C10 | ODE integration algorithm | Stiff vs. non-stiff approaches, Adams-Moulton vs. BDF |

C11 | Integration accuracy | ODE integrator tolerances |

C12 | Derivative calculation | Finite differences, sensitivity equations, adjoint sensitivities |

C13 | Stopping rule | Optimization termination criteria |

C14 | Handling of non-converging ODE integration | Termination of optimization vs. infinite loss |

C15 | Algorithm-specific configurations | Cross-over rate, annealing temperature, number of particles |