Report
A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
| العنوان: | A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints |
|---|---|
| المؤلفون: | Tuan, Tran Anh, Dung, Nguyen Viet, Thang, Tran Ngoc |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Mathematics - Optimization and Control |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.05955 |
| رقم الانضمام: | edsarx.2402.05955 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |