On the linearization of separable quadratic constraints in dual sequential convex programs
Date
2012
Authors
Groenwold A.A.
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Abstract
We 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.
Description
Keywords
Diagonal quadratic approximation, Duality, Linearization, Quadratic program (QP), Separable, Structural optimization
Citation
Computers and Structures
102-103
42
48
102-103
42
48