New insights into the NLP-Id bound for maximum-entropy sampling

Kurt Anstreicher; Marcia Fampa; Jon Lee; Yongchun Li; Gabriel Ponte

doi:10.48550/arxiv.2609.01214

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New insights into the NLP-Id bound for maximum-entropy sampling

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New insights into the NLP-Id bound for maximum-entropy sampling

Kurt Anstreicher, Marcia Fampa, Jon Lee, Yongchun Li and Gabriel Ponte

arXiv

09/01/2026

DOI: 10.48550/arxiv.2609.01214

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Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

We establish new properties of the NLP-Id upper bound for the maxi\-{m}{u}{m}-entropy sampling problem (MESP). In particular, we give a detailed look at the concavity of its objective function as a function of the scaling parameter employed for NLP bounds for MESP. This leads to more relaxed choices for the scaling parameter for NLP-Id and even improved upper bounds for MESP.

Mathematics - Optimization and Control

Details

Title: Subtitle: New insights into the NLP-Id bound for maximum-entropy sampling
Creators: Kurt Anstreicher
Marcia Fampa
Jon Lee
Yongchun Li
Gabriel Ponte
Resource Type: Preprint
Publication Details: arXiv
DOI: 10.48550/arxiv.2609.01214
ISSN: 2331-8422
Publisher: arXiv
Language: English
Date posted: 09/01/2026
Academic Unit: Business Analytics
Record Identifier: 9985221842302771

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