A global optimization method, αBB, for process design

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

doi:10.1016/0098-1354(96)00080-4

A global optimization method, αBB, for process design

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

Research output: Contribution to journal › Article › peer-review

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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 language	English (US)
Pages (from-to)	S419-S424
Journal	Computers and Chemical Engineering
Volume	20
Issue number	SUPPL.1
DOIs	https://doi.org/10.1016/0098-1354(96)00080-4
State	Published - 1996

All Science Journal Classification (ASJC) codes

General Chemical Engineering
Computer Science Applications

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10.1016/0098-1354(96)00080-4

Cite this

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title = "A global optimization method, αBB, for process design",

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.",

author = "Adjiman, {C. S.} and Androulakis, {I. P.} and Maranas, {C. D.} and Floudas, {C. A.}",

year = "1996",

doi = "10.1016/0098-1354(96)00080-4",

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T1 - A global optimization method, αBB, for process design

AU - Adjiman, C. S.

AU - Androulakis, I. P.

AU - Maranas, C. D.

AU - Floudas, C. A.

PY - 1996

Y1 - 1996

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AB - 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.

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A global optimization method, αBB, for process design

Abstract

All Science Journal Classification (ASJC) codes

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