1980
DOI: 10.1080/03601218008907358
|View full text |Cite
|
Sign up to set email alerts
|

Design Sensitivity Analysis in Structural Mechanics.II. Eigenvalue Variations

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
19
0
2

Year Published

1983
1983
2014
2014

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 115 publications
(22 citation statements)
references
References 5 publications
1
19
0
2
Order By: Relevance
“…Note that (13) is similar to the standard results of eigenvalue optimization (Haug and Rousselet 1980;Seyranian et al 1994;de Gournay 2006), and that (14) incorporates jump terms that are typical of shape sensitivity with discontinuous coefficients (Bernardi and Pironneau 2003;Pantz 2005). Note also that (13) holds for any eigenvalue (and not only the first one) and in the case of a simple eigenvalue dλ is linear w.r.to θ , hence Fréchet differentiability holds.…”
Section: Statement Of the Main Resultssupporting
confidence: 52%
“…Note that (13) is similar to the standard results of eigenvalue optimization (Haug and Rousselet 1980;Seyranian et al 1994;de Gournay 2006), and that (14) incorporates jump terms that are typical of shape sensitivity with discontinuous coefficients (Bernardi and Pironneau 2003;Pantz 2005). Note also that (13) holds for any eigenvalue (and not only the first one) and in the case of a simple eigenvalue dλ is linear w.r.to θ , hence Fréchet differentiability holds.…”
Section: Statement Of the Main Resultssupporting
confidence: 52%
“…In the previous relation, we can use either h or −h, therefore the minimality of Ω * gives the desired result (14), (15). Now, let us consider the case of a segment Σ.…”
Section: Optimality Conditionsmentioning
confidence: 99%
“…Let us denote by v = V.n such a concave function. Replacing in (22) and using (14), (15) yields on the segment Σ:…”
Section: Optimality Conditionsmentioning
confidence: 99%
“…For prior attempts in this context see Haug and Rousselet [18] and Choi and Haug [7]. Our principal tool is the generalized gradient of Clarke [10].…”
Section: Necessary Conditionsmentioning
confidence: 99%