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A Sparse Superlinearly Convergent SQP with Applications to Two-dimensional Shape
 

Summary: A Sparse Superlinearly Convergent SQP with
Applications to Two-dimensional Shape
Optimization
Mihai Anitescu
Mathematics and Computer Science Division, Argonne National Laboratory
9700 South Cass Avenue, Argonne, Illinois 60439
E-mail: anitescu@mcs.anl.gov
Radu Serban
Department of Mechanical Engineering, The University of Iowa
ERF 229, Iowa City, Iowa 52242
E-mail: rserban@ccad.uiowa.edu
December 21, 2006
Abstract
Discretization of optimal shape design problems leads to very large
nonlinear optimization problems. For attaining maximum computa-
tional efficiency, a sequential quadratic programming (SQP) algorithm
should achieve superlinear convergence while preserving sparsity and
convexity of the resulting quadratic programs. Most classical SQP ap-
proaches violate at least one of the requirements. We show that, for
a very large class of optimization problems, one can design SQP algo-

  

Source: Anitescu, Mihai - Mathematics and Computer Science Division, Argonne National Laboratory

 

Collections: Mathematics