A class of globally convergent optimization methods based on conservative convex separable approximations
2001 (English)In: SIAM Journal on Optimization, ISSN 1052-6234, E-ISSN 1095-7189, Vol. 12, no 2, 555-573 p.Article in journal (Refereed) Published
This paper deals with a certain class of optimization methods, based on conservative convexseparable approximations (CCSA), for solving inequality-constrained nonlinear programming problems. Each generated iteration point is a feasible solution with lower objective value than the previous one, and it is proved that the sequence of iteration points converges toward the set of Karush-Kuhn Tucker points. A major advantage of CCSA methods is that they can be applied to problems with a very large number of variables (say 10(4) 10(5)) even if the Hessian matrices of the objective and constraint functions are dense.
Place, publisher, year, edition, pages
2001. Vol. 12, no 2, 555-573 p.
nonlinear programming, constrained minimization, convex approximations, method of moving asymptotes
IdentifiersURN: urn:nbn:se:kth:diva-21287ISI: 000173578400013OAI: oai:DiVA.org:kth-21287DiVA: diva2:339985
QC 201005252010-08-102010-08-10Bibliographically approved