A global optimization method, αBB, for process design

C. S. Adjiman, I. P. Androulakis, C. D. Maranas, C. A. Floudas

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

Abstract

A global optimization algorithm, αBB, for twice-differentiable NLPs is presented. It operates within a branch-and-bound framework and requires the construction of a convex lower bound-ing problem. A technique to generate such a valid convex underestimator for arbitrary twice-differentiable functions is described. The αBB has been applied to a variety of problems and a summary of the results obtained is provided.

Original languageEnglish (US)
Pages (from-to)S419-S424
JournalComputers and Chemical Engineering
Volume20
Issue numberSUPPL.1
DOIs
StatePublished - 1996
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Fingerprint Dive into the research topics of 'A global optimization method, αBB, for process design'. Together they form a unique fingerprint.

Cite this