  Pareto Front Learning (PFL) was recently introduced as an efficient method
for approximating the entire Pareto front, the set of all optimal solutions to
a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping
between a preference vector and a Pareto optimal solution is still ambiguous,
rendering its results. This study demonstrates the convergence and completion
aspects of solving MOO with pseudoconvex scalarization functions and combines
them into Hypernetwork in order to offer a comprehensive framework for PFL,
called Controllable Pareto Front Learning. Extensive experiments demonstrate
that our approach is highly accurate and significantly less computationally
expensive than prior methods in term of inference time.
