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PreprintarXiv licencearXiv

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

  • Kurt Anstreicher
  • Marcia Fampa
  • Jon Lee
  • Yongchun Li
  • Gabriel Ponte
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Abstract

We establish new properties of the NLP-Id upper bound for the maxi\-mum-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.

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Cite

BibTeXRIS
BibTeX
@misc{anstreicher2026insights,
  title = {{New insights into the NLP-Id bound for maximum-entropy sampling}},
  author = {Kurt Anstreicher and Marcia Fampa and Jon Lee and Yongchun Li and Gabriel Ponte},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.01214},
  eprint = {2609.01214},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.01214},
}
RIS
TY  - UNPB
TI  - New insights into the NLP-Id bound for maximum-entropy sampling
AU  - Kurt Anstreicher
AU  - Marcia Fampa
AU  - Jon Lee
AU  - Yongchun Li
AU  - Gabriel Ponte
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.01214
UR  - https://arxiv.org/abs/2609.01214
AB  - We establish new properties of the NLP-Id upper bound for the maxi\-mum-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.
ER  -