DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
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Proposes six variants of individual minima-informed DM for MOMPC, including two novel ones, and embeds them in a stabilizing framework with a less restrictive descent condition than prior work.
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Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
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Individual Minima-Informed Multi-Objective Model Predictive Control for Fixed Point Stabilization
Proposes six variants of individual minima-informed DM for MOMPC, including two novel ones, and embeds them in a stabilizing framework with a less restrictive descent condition than prior work.