Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Level
Multi-objective bilevel optimization has wide applications in the AI area such as automated learning and multi-task meta-learning. Although recently some works have been begun to study the multi-objective bilevel optimization, the proposed methods rely on the (strongly) convex lower level problems. In fact, these multi-objective bilevel learning problems are generally nonconvex, and particularly their lower level problems are nonconvex. To fill this gap, we propose a class of Multi-Objective Moreau Envelope based Hessian-free Algorithms (MOMEHA) for the multi-objective bilevel learning problems with nonconvex lower level. Specifically, our method uses the Moreau envelope to relax the orig...
| Authors | Yicong Jiang, Feihu Huang |
|---|---|
| Primary category | math.OC |
| Published | 13 August 2026 |
| Last updated at source | 5 September 2026 |
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