How to extract Pareto optimal parameters in constrained MOO?

Hello, I am using Ax’s Service API for constrained multiobjective optimisation. I have recently run into a problem where applying get_pareto_optimal_parameters() returns an error message stating that this functionality is still under development for the constrained optimisation. I was wondering if there are any workarounds which I could use to at least approximate the optimal point in the optimisation. I was considering applying the simple TOPSIS algorithm to points generated with the compute_posterior_pareto_frontier (excluding the out-of-design points) to find the best point in two objectives and then extracting the correspondent parameterisation. Is this a feasible approach? If so, how could I associate a point in the frontier with a parametrisation?

[IgorKuszczak]

Hi @bernardbeckerman, I am using thresholds in both objectives: 0.9 for the ‘displacement’ (minimized) and 30 for the ‘weight_savings’ (maximized). As you can see in the picture below, there are multiple arms which exceed all objective thresholds.

[bernardbeckerman]

@IgorKuszczak are you using objective thresholds in your multi-objective optimization config? If so, do any of your experiment’s arms exceed all objectives? If no arms exceed the thresholds for all objectives, there might be zero points on the Pareto curve. Please let me know if it looks like this may be the case.