On the linearization of separable quadratic constraints in dual sequential convex programs

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dc.contributor.authorGroenwold A.A.
dc.date.accessioned2012-06-06T08:02:28Z
dc.date.available2012-06-06T08:02:28Z
dc.date.issued2012
dc.description.abstractWe study the replacement of dual subproblems based on separable quadratic objective and separable quadratic constraint functions by classical separable quadratic programs, in which the constraints are linearized. The quadratic subprograms are then solved in the dual space, which allows for a direct assessment of the computational implications that results from linearization of the separable quadratic constraints in the first place. The solution of the linearized QP forms in the dual space seems far easier than the solution of their quadratic-quadratic counterparts, which may have important implications for algorithms aimed at very large scale optimal design. © 2012 Elsevier Ltd. All rights reserved.
dc.identifier.citationComputers and Structures
dc.identifier.citation102-103
dc.identifier.citation42
dc.identifier.citation48
dc.identifier.issn457949
dc.identifier.otherdoi:10.1016/j.compstruc.2012.03.014
dc.identifier.urihttp://hdl.handle.net/10019.1/21312
dc.subjectDiagonal quadratic approximation
dc.subjectDuality
dc.subjectLinearization
dc.subjectQuadratic program (QP)
dc.subjectSeparable
dc.subjectStructural optimization
dc.titleOn the linearization of separable quadratic constraints in dual sequential convex programs
dc.typeArticle
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