The algorithm Argument

Currently we support the following alorihms, ordered according to the libarry from which they come originally.

pygmo

• "pygmo_gaco"

• "pygmo_de"

• "pygmo_sade"

• "pygmo_de1220"

• "pygmo_ihs"

• "pygmo_pso"

• "pygmo_pso_gen"

• "pygmo_sea"

• "pygmo_sga"

• "pygmo_simulated_annealing"

• "pygmo_bee_colony"

• "pygmo_cmaes"

• "pygmo_xnes"

• "pygmo_nsga2"

• "pygmo_moead"

nlopt

• "nlopt_cobyla"

• "nlopt_bobyqa"

• "nlopt_newuoa"

• "nlopt_newuoa_bound"

• "nlopt_praxis"

• "nlopt_neldermead"

• "nlopt_sbplx"

• "nlopt_mma"

• "nlopt_ccsaq"

• "nlopt_slsqp"

• "nlopt_lbfgs"

• "nlopt_tnewton_precond_restart"

• "nlopt_tnewton_precond"

• "nlopt_tnewton_restart"

• "nlopt_tnewton"

• "nlopt_var2"

• "nlopt_var1"

• "nlopt_auglag"

• "nlopt_auglag_eq"

scipy

• "scipy_L-BFGS-B"

• "scipy_TNC"

• "scipy_SLSQP"

tao

• "pounders"

The algo_options Argument

algo_options is a dictionary with optional keyword arguments that are passed to the optimizer. This includes tolerances for the termination criteria, parameters that determine how greedy the optimizer is or the stepsize for a numerical gradient. It is the only thing in estimagic that is specific to each algorithm.

Typically you will leave all of those parameters at their default, unless you have a very difficult optimization problem. If so, you can find all available options at the following links (depending on the origin of the algorithm):

• pygmo

• nlopt

• scipy

The algo_options of the pounders algorithm can be found in the documentation of The Pounders Algorithm