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Mixed Integer Goal Programming for Personalized Meal Optimization with User-Defined Serving Granularity

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arXiv:2605. 13849v1 Announce Type: new Abstract: Determining what to eat to satisfy nutritional requirements is one of the oldest optimization problems in operations research, yet existing formulations have two persistent limitations: continuous variables produce impractical fractional servings (1.

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Authors: Francisco Aguilera Moreno
Released: 2026-05-16 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Mixed Integer Goal Programming for Personalized Meal Optimization with User-Defined Serving Granularity overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, persona, under, soft, eval, source, rate, implement. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.13849

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