Highlighting Named Entities in Input for Auto-formulation of Optimization Problems

Neeraj Gangwar, Nickvash Kani

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical software, formulating a problem as a set of mathematical operations has been typically done manually by domain experts. Recent machine learning methods have shown promise in converting textual problem descriptions to corresponding mathematical formulations. This paper presents an approach that converts linear programming word problems into mathematical formulations. We leverage the named entities in a problem description and augment the input to highlight these entities. Our approach achieves the highest accuracy among all submissions to the NL4Opt competition, securing first place in the generation track.

Original languageEnglish (US)
Title of host publicationIntelligent Computer Mathematics - 16th International Conference, CICM 2023, Proceedings
EditorsCatherine Dubois, Manfred Kerber
Number of pages12
ISBN (Print)9783031427527
StatePublished - 2023
EventProceedings of the 16th Conference on Intelligent Computer Mathematics, CICM 2023 - Cambridge, United Kingdom
Duration: Sep 5 2023Sep 8 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14101 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceProceedings of the 16th Conference on Intelligent Computer Mathematics, CICM 2023
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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