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Sequential Quadratic Programming Methods for Large-Scale Problems

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Computational Issues in High Performance Software for Nonlinear Optimization

Abstract

Sequential quadratic (SQP) programming methods are the method of choice when solving small or medium-sized problems. Since they are complex methods they are difficult (but not impossible) to adapt to solve large-scale problems. We start by discussing the difficulties that need to be addressed and then describe some general ideas that may be used to resolve these difficulties. A number of SQP codes have been written to solve specific applications and there is a general purposed SQP code called SNOPT, which is intended for general applications of a particular type. These are described briefly together with the ideas on which they are based. Finally we discuss new work on developing SQP methods using explicit second derivatives.

Research supported by the National Science Foundation Grant DMI-9500668; the Office of Naval Research Grant N00014-96-1-0274.

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Authors and Affiliations

  1. Systems Optimization Laboratory, Department of Operations Research, Stanford University, USA

    Walter Murray

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  1. Walter Murray
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  1. Universitià di Napoli “Federico II”, Italy

    Almerico Murli  & Gerardo Toraldo  & 

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© 1997 Kluwer Academic Publishers

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Murray, W. (1997). Sequential Quadratic Programming Methods for Large-Scale Problems. In: Murli, A., Toraldo, G. (eds) Computational Issues in High Performance Software for Nonlinear Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-26778-4_8

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  • DOI: https://doi.org/10.1007/978-0-585-26778-4_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-9862-2

  • Online ISBN: 978-0-585-26778-4

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