Strong valid inequalities for Boolean logical pattern generation

Kedong Yan, Hong Seo Ryoo

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


0–1 multilinear programming (MP) captures the essence of pattern generation in logical analysis of data (LAD). This paper utilizes graph theoretic analysis of data to discover useful neighborhood properties among data for data reduction and multi-term linearization of the common constraint of an MP pattern generation model in a small number of stronger valid inequalities. This means that, with a systematic way to more efficiently generating Boolean logical patterns, LAD can be used for more effective analysis of data in practice. Mathematical properties and the utility of the new valid inequalities are illustrated on small examples and demonstrated through extensive experiments on 12 real-life data mining datasets.

Original languageEnglish
Pages (from-to)183-230
Number of pages48
JournalJournal of Global Optimization
Issue number1
Publication statusPublished - 2017 Sept 1

Bibliographical note

Publisher Copyright:
© 2017, Springer Science+Business Media New York.


  • 0–1 linearization
  • 0–1 multilinear programming
  • Boolean logic
  • Clique
  • Hypercube
  • Logical analysis of data
  • Pattern

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics


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