Support for the `infeasible problem detected`. about l4casadi HOT 3 CLOSED

Hi,

Infeasible_Problem_Detected is the solver struggling. This can be due to your model being evaluated in areas where it produces unexpected results.

As an example, your training data might include input x=1 and x=1.1 and will produce the right result but, in theory, could produce the completely wrong result for x=1.05, which your analytical model will not. This might not be exactly the case but it demonstrates how a trained model can diverge from an analytical model.

Best
Tim

from l4casadi.

To check if your constraints are satisfied, you can add slack variables to each constraint and minimize them by including the slack variables as part of the cost function. Ideally, all slack variables should be zero.

from l4casadi.

Thanks. Indeed, both the accuracy of the learned model and the formulation of the problem will affect the final gradient-based solver to converge.

from l4casadi.