A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
<|reference_start|>A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints: 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.<|reference_end|>
@article{tuan2024a,
    title={A Hyper-Transformer model for Controllable Pareto Front Learning with
  Split Feasibility Constraints},
    author={Tran Anh Tuan, Nguyen Viet Dung, Tran Ngoc Thang},
    journal={arXiv preprint arXiv:2402.05955},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05955},
    primaryClass={cs.LG math.OC}
}