Learning to optimize by multi-gradient for multi-objective optimization

• URL: http://arxiv.org/abs/2311.00559v1
• Date: Wed, 1 Nov 2023 14:55:54 GMT
• Title: Learning to optimize by multi-gradient for multi-objective optimization
• Authors: Linxi Yang, Xinmin Yang, Liping Tang
• Abstract summary: We introduce a new automatic learning paradigm for optimizing MOO problems, and propose a multi-gradient learning to optimize (ML2O) method. As a learning-based method, ML2O acquires knowledge of local landscapes by leveraging information from the current step. We show that our learned outperforms hand-designed competitors on training multi-task learning (MTL) neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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