A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints

• URL: http://arxiv.org/abs/2402.05955v1
• Date: Sun, 4 Feb 2024 10:21:03 GMT
• Title: A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
• Authors: Tran Anh Tuan, Nguyen Viet Dung, Tran Ngoc Thang
• Abstract summary: We develop a hyper-transformer (Hyper-Trans) model for CPFL with SFC. We show that the Hyper-Trans model makes MED errors smaller in computational experiments than the Hyper-MLP model.
• Score: 2.07180164747172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controllable Pareto front learning (CPFL) approximates the Pareto solution set and then locates a Pareto optimal solution with respect to a given reference vector. However, decision-maker objectives were limited to a constraint region in practice, so instead of training on the entire decision space, we only trained on the constraint region. Controllable Pareto front learning with Split Feasibility Constraints (SFC) is a way to find the best Pareto solutions to a split multi-objective optimization problem that meets certain constraints. In the previous study, CPFL used a Hypernetwork model comprising multi-layer perceptron (Hyper-MLP) blocks. With the substantial advancement of transformer architecture in deep learning, transformers can outperform other architectures in various tasks. Therefore, we have developed a hyper-transformer (Hyper-Trans) model for CPFL with SFC. We use the theory of universal approximation for the sequence-to-sequence function to show that the Hyper-Trans model makes MED errors smaller in computational experiments than the Hyper-MLP model.

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