GAPSPLIT: Efficient random sampling for non-convex constraint-based models

Thomas C. Keaty, Thomas C. Keaty, Paul A. Jensen, Paul A. Jensen, Paul A. Jensen

Research output: Contribution to journalArticlepeer-review

Abstract

GAPSPLIT generates random samples from convex and non-convex constraint-based models by targeting under-sampled regions of the solution space. GAPSPLIT provides uniform coverage of linear, mixed-integer and general non-linear models.

Original languageEnglish (US)
Pages (from-to)2623-2625
Number of pages3
JournalBioinformatics
Volume36
Issue number8
DOIs
StatePublished - Apr 15 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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