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First and second order convex approximation strategies in structural optimizationIn this paper, various methods based on convex approximation schemes are discussed that have demonstrated strong potential for efficient solution of structural optimization problems. First, the convex linearization method (Conlin) is briefly described, as well as one of its recent generalizations, the method of moving asymptotes (MMA). Both Conlin and MMA can be interpreted as first-order convex approximation methods that attempt to estimate the curvature of the problem functions on the basis of semiempirical rules. Attention is next directed toward methods that use diagonal second derivatives in order to provide a sound basis for building up high-quality explicit approximations of the behavior constraints. In particular, it is shown how second-order information can be effectively used without demanding a prohibitive computational cost. Various first-order and second-order approaches are compared by applying them to simple problems that have a closed form solution.
Document ID
19890060916
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Fleury, C.
(California, University Los Angeles, United States)
Date Acquired
August 14, 2013
Publication Date
June 1, 1989
Publication Information
Publication: Structural Optimization
Volume: 1
ISSN: 0934-4373
Subject Category
Structural Mechanics
Accession Number
89A48287
Funding Number(s)
CONTRACT_GRANT: NSG-1490
Distribution Limits
Public
Copyright
Other

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