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Sequential Quadratic Programming Methods for Large-Scale Problems

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Abstract

Sequential quadratic (SQP) programming methodsare the method of choice when solving small or medium-sized problems. Sincethey are complex methods they are difficult (but not impossible) to adapt tosolve large-scale problems. We start by discussing the difficulties that needto be addressed and then describe some general ideas that may be used toresolve 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 aredescribed briefly together with the ideas on which they are based. Finally wediscuss new work on developing SQP methods using explicit second derivatives.

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

    Walter Murray

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Murray, W. Sequential Quadratic Programming Methods for Large-Scale Problems. Computational Optimization and Applications 7, 127–142 (1997). https://doi.org/10.1023/A:1008671829454

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