Feb. 12, 2024, 5:41 a.m. | Tran Anh Tuan Nguyen Viet Dung Tran Ngoc Thang

cs.LG updates on arXiv.org arxiv.org

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 …

constraints cs.lg decision maker math.oc pareto practice reference set solution space training transformer transformer model vector

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