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