# A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

## TL;DR

This paper introduces a comprehensive framework for Pareto Front Learning that leverages pseudoconvex scalarization functions and Hypernetworks, achieving high accuracy and efficiency in approximating Pareto fronts for multi-objective optimization.

## Contribution

It presents a novel framework combining pseudoconvex scalarization and Hypernetworks for controllable Pareto front learning, addressing ambiguity and improving efficiency.

## Key findings

- High accuracy in Pareto front approximation
- Significantly reduced inference time
- Effective handling of preference vector mapping

## Abstract

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.