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Convergence depth control for interior point methods
Author(s) -
Chen Weifeng,
Shao Zhijiang,
Wang Kexin,
Chen Xi,
Biegler Lorenz T.
Publication year - 2010
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.12225
Subject(s) - hessian matrix , interior point method , computation , mathematical optimization , convergence (economics) , interface (matter) , quadratic programming , point (geometry) , computer science , scaling , quadratic equation , sequential quadratic programming , mathematics , algorithm , geometry , bubble , maximum bubble pressure method , parallel computing , economics , economic growth
For practical applications, optimization algorithms may converge to the optimal solution unreasonably slowly because of factors such as the poor scaling, ill‐conditioning, errors in calculation, and so on. Most improvements during the optimization procedure are made within a small part of the total computation time. To relieve the heavy computational burden, it is necessary to balance the calculation accuracy and computation cost. The traditional termination criteria based on the Karush‐Kuhn‐Tucker conditions cannot appropriately meet this requirement. Convergence depth control (CDC) strategy for Reduced Hessian Successive Quadratic Programming (RSQP) was presented as an alternative measure in a previous study. This work incorporates interior point methods with the modified CDC strategy, which was tested through AMPL interface and Aspen Open Solvers interface. Related properties are proved. © 2010 American Institute of Chemical Engineers AIChE J, 2010