Constraints tolerance IPNewton in Optim.jl

Specific Domains Optimization (Mathematical)
1

Hello,

I was wondering if it is possible to specify up to which tolerance the equality constraints are satisfied in Optim.jl using IPNewton method.

Currently, the optimization stops because x_tol or f_tol are satisfied, but not because g_tol is satisfied. If I manually check the value of the constraints, they are satisfied within a tolerance of 1e-2. Is it possible to set a parameter to force higher precision?

I have tried setting x_tol=NaN and f_tol=NaN to force the algorithm to stop by g_tol, but then it stagnates:

Iter     Lagrangian value Function value   Gradient norm    |==constr.|      μ
     0   3.332750e+01     1.746610e+00     3.438553e+01     1.901604e+01     1.00e+00
 * time: 1.67178566274176e9
     1   1.353145e+01     1.702241e+00     3.543331e+01     1.048336e+01     1.00e-01
 * time: 1.671785662797074e9
     2   4.377825e+00     1.699334e+00     7.754110e+02     3.833958e-01     9.76e-02
 * time: 1.671785662809908e9
     3   2.529626e+00     1.699267e+00     5.851341e+01     3.768007e-01     2.01e-02
 * time: 1.671785662883332e9
     4   2.278308e+00     1.703248e+00     8.222396e+00     1.410696e-01     1.99e-02
 * time: 1.671785662899112e9
     5   1.965445e+00     1.712061e+00     2.599936e+00     1.133042e-03     1.12e-02
 * time: 1.67178566292044e9
     6   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.67178566309099e9
     7   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663162644e9
     8   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663237643e9
     9   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663344027e9
    10   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663460111e9
    11   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663539827e9
    12   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663623636e9
    13   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663723319e9
    14   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663871442e9
    15   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785663952723e9
    16   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664022078e9
    17   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664093651e9
    18   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664163002e9
    19   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664231979e9
    20   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664306802e9
    21   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664431646e9
    22   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664514846e9
    23   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.671785664598036e9
    24   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.67178566466456e9
    25   1.766109e+00     1.712061e+00     4.019143e+00     1.133042e-03     2.35e-03
 * time: 1.67178566474315e9

Why doesn’t it try to reduce the value of μ and see if it can improve the norm of the gradient?

I apologize for not posting the code (it is a research code and it has not been published yet).

Thank you very much,

SHCE