The default parameter estimation procedure of the d2d-framework uses a deterministic optimisation algorithm (LSQNONLIN) based on evaluated sensitivities, which are integrated together with the ODE system. This approach is justify in  to be the most efficient and reliable for typical problems in Systems Biology. The initial parameters in a multi-start setup are taken from a Latin hypercube sampling.
However, plenty of other optimisation algorithms which are described in  can be utilised, too. These comprise stochastic methods, e.g. different realisations of particle swarm optimisation, evolutionary strategies and hill climbing strategies. They are accessible via
with the index ranging from one to thirteen for the different methods described in EvA2 Optimization Framework.
In addition, several deterministic optimisation procedures and functions are available. Currently, seven optimizers are available:
>> ar.config.optimizers ans = 'lsqnonlin' 'fmincon' 'PSO' 'STRSCNE' 'arNLS' 'fmincon_as_lsq' 'arNLS_SR1' 'NL2SOL' 'TRESNEI' 'Ceres'
The default optimizer
lsqnonlin is chosen by
ar.config.optimizer = 1;
fmincon is chosen by
ar.config.optimizer = 2;
An implementation of particle-swarm optimization is available via
ar.config.optimizer = 3;
Custom self-written optimization routines like
arNLS_SR1 can be chosen by setting
7. These examples also show how users implement their own optimization techniques (see
ar.config.optim coincides with Matlab's optimization struct which is specified by the standard Matlab function
optimset or returned by
Third party optimization routines with self-written interfaces include
Ceres. They can be chosen by setting
The optimization struct for Googles Ceres non-linear solver is specified in
ar.config.optimceres. When encountering problems while compiling or running Ceres check out the wiki page
Solving issues with Ceres
 Raue A., et al. Lessons Learned from Quantitative Dynamical Modeling in Systems Biology. PLOS ONE, 8(9), e74335, 2013.