Hello,

I am using the Python interface to implement a Nonlinear MPC. As a solver, I use `PARTIAL_CONDENSING_HPIPM`

and `SQP_RTI`

. For parameter tuning, my goal is to run a simulation of the system multiple times without re-compiling acados each time. The initial conditions stay the same, however I need to change the cost function weights and the slack term weights.

To do so, I initialize the solver with an arbitrary parametrization (e.g. unit matrices). For running a simulation, I reset all solver parameters to their initial state by restoring the initial iterate with `load_iterate()`

. From what I can see, the solver state should be identical at each start of the simulation.

I then set the relevant parameters using the `cost_set()`

method:

```
# assign cost function weights
Q = np.diag([parametrization['q_xy'],
parametrization['q_xy'],
parametrization['q_yaw'],
parametrization['q_vel']])
R = np.diag([parametrization['r_jerk'],
parametrization['r_steering_rate']])
Qe = Q
# assign slack term penalties
L1 = parametrization['L1']
L2 = parametrization['L2']
W = scipy.linalg.block_diag(Q, R)
We = Qe
# update cost function weights
for i in range(N):
solver.cost_set(i, 'W', W)
solver.cost_set(N, 'W', We)
# update slack terms
z = np.ones((2,)) * L1
Z = np.ones((2,)) * L2
solver.cost_set(0, 'zl', z)
solver.cost_set(0, 'zu', z)
solver.cost_set(0, 'Zl', Z)
solver.cost_set(0, 'Zu', Z)
solver.cost_set(N, 'zl', z)
solver.cost_set(N, 'zu', z)
solver.cost_set(N, 'Zl', Z)
solver.cost_set(N, 'Zu', Z)
z = np.ones((3,)) * L1
Z = np.ones((3,)) * L2
for i in range(1, N):
solver.cost_set(i, 'zl', z)
solver.cost_set(i, 'zu', z)
solver.cost_set(i, 'Zl', Z)
solver.cost_set(i, 'Zu', Z)
```

However, the solver depends strongly on the chosen initial parametrization - for parametrizations that are similar to the initial one, a solution is found. However, if the initial values differ strongly, the solver fails to find a solution even for previously sucessful parameters.

Additionally, it appears that after having crashed once, the solver is not able to find a solution anymore. Let’s say parametrization A is feasible and B is not. When using A, B and then A again, only the first trial of A can be solved.

I suppose that I do not fully reset the solver, leading to some information being conserved and interfering with the next trial. However I have been unable to encounter the error. Does anybody have experience with this behaviour? I would be thankful for any help.

Thanks in advance!