Learning to optimize by multi-gradient for multi-objective optimization
Linxi Yang Xinmin Yang Liping Tang
The development of artificial intelligence (AI) for science has led to the
emergence of learning-based research paradigms, necessitating a compelling
reevaluation of the design of multi-objective optimization (MOO) methods. The
new generation MOO methods should be rooted in automated learning rather than
manual design. In this paper, we introduce a new automatic learning paradigm
for optimizing MOO problems, and propose a multi-gradient learning to optimize
(ML2O) method, which automatically learns a generator (or mappings) from
multiple gradients to update directions. As a learning-based method, ML2O
acquires knowledge of local landscapes by leveraging information from the
current step and incorporates global experience extracted from historical
iteration trajectory data. By introducing a new guarding mechanism, we propose
a guarded multi-gradient learning to optimize (GML2O) method, and prove that
the iterative sequence generated by GML2O converges to a Pareto critical point.
The experimental results demonstrate that our learned optimizer outperforms
hand-designed competitors on training multi-task learning (MTL) neural network.