A sparse superlinearly convergent SQP with applications to two-dimensional shape optimization.

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Discretization of optimal shape design problems leads to very large nonlinear optimization problems. For attaining maximum computational efficiency, a sequential quadratic programming (SQP) algorithm should achieve superlinear convergence while preserving sparsity and convexity of the resulting quadratic programs. Most classical SQP approaches violate at least one of the requirements. We show that, for a very large class of optimization problems, one can design SQP algorithms that satisfy all these three requirements. The improvements in computational efficiency are demonstrated for a cam design problem.

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15 p.

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Anitescu, M. April 15, 1998.

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Description

Discretization of optimal shape design problems leads to very large nonlinear optimization problems. For attaining maximum computational efficiency, a sequential quadratic programming (SQP) algorithm should achieve superlinear convergence while preserving sparsity and convexity of the resulting quadratic programs. Most classical SQP approaches violate at least one of the requirements. We show that, for a very large class of optimization problems, one can design SQP algorithms that satisfy all these three requirements. The improvements in computational efficiency are demonstrated for a cam design problem.

Physical Description

15 p.

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OSTI as DE00010738

Medium: P; Size: 15 pages

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  • 1998 ASME Design Engineering Technical Conference and Computers in Engineering Conference, Atlanta, GA (US), 09/13/1998--09/16/1998

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  • Report No.: ANL/MCS/CP-96123
  • Grant Number: W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 10738
  • Archival Resource Key: ark:/67531/metadc624806

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  • April 15, 1998

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  • June 16, 2015, 7:43 a.m.

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  • April 7, 2017, 3:11 p.m.

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Anitescu, M. A sparse superlinearly convergent SQP with applications to two-dimensional shape optimization., article, April 15, 1998; Illinois. (digital.library.unt.edu/ark:/67531/metadc624806/: accessed October 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.