Is it possible to add as a dynamic constraint an external custom function in Mixed Integer Nonlinear Programming libraries in Python? I am working with boolean variables and Numpy matrices (size m x n) where i want to minimize the sum of total values requested (e.g tot_vals = 2,3......n). Therefore, i want to add some "spatial" constraints, I've created the functions (based on Boolean indexing) and i try to implement them in my optimization procedure. In CVXPY, it fails as i can only add CVXPY's formatted constraints (as far as i know), PULP fails as it works only for LP problems, maybe a choice could be Pyomo, OpenOpt or PySCIPopt?
Thank you in advance for your help
With PySCIPOpt this is possible. You would need to create a custom constraint handler which checks the current LP solution for feasibility and maybe adds valid inequalities to circumvent the infeasibility in the next node.
One example of this procedure is the TSP implementation in PySCIPOpt. This is also explained in some more detail in this tutorial article about PySCIPOpt.