Hi, there are a few things about IPOPT options, and I just wanted to bring them to your attention.
1. Convergence tolerance
In Moco, set_optim_convergence_tolerance would set an identical value for the following options in IPOPT, while their default values vary according to the IPOPT docs. For instance, 1e-3 would be greater than the default acceptable_tol, compl_inf_tol, and tol values, while it is smaller than the default acceptable_compl_inf_tol, acceptable_dual_inf_tol, and dual_inf_tol values. I wonder if there is a smarter and more robust way for this.
In a few resources and other projects (such as Link 1, Link 2, Link 3, Link 4), it's been said that using adaptivemu_strategy can lead to better convergence and improved performance by reducing the number of iterations (in some cases). Currently, it's not available in MocoCasADiSolver, and I was just curious to see whether this option could be beneficial for Moco or not.
Hi, there are a few things about IPOPT options, and I just wanted to bring them to your attention.
1. Convergence tolerance
In Moco, set_optim_convergence_tolerance would set an identical value for the following options in IPOPT, while their default values vary according to the IPOPT docs. For instance,
1e-3
would be greater than the defaultacceptable_tol
,compl_inf_tol
, andtol
values, while it is smaller than the defaultacceptable_compl_inf_tol
,acceptable_dual_inf_tol
, anddual_inf_tol
values. I wonder if there is a smarter and more robust way for this.2. Constraint tolerance
The same for set_optim_constraint_tolerance.
3. Adaptive mu_strategy
In a few resources and other projects (such as Link 1, Link 2, Link 3, Link 4), it's been said that using
adaptive
mu_strategy
can lead to better convergence and improved performance by reducing the number of iterations (in some cases). Currently, it's not available inMocoCasADiSolver
, and I was just curious to see whether this option could be beneficial for Moco or not.Thank you.