On the convergence of successive linear quadratic programming algorithms

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Persistent URL	http://purl.org/net/epubs/work/33858
Record Status	Checked
Record Id	33858
Title	On the convergence of successive linear quadratic programming algorithms
Contributors	RH Byrd, N I M Gould, J Nocedal, RA Waltz
Abstract	The global convergence properties of a class of penalty methods for nonlinear programming are analyzed. These methods include successive linear programming approaches, and more specifically, the successive linear-quadratic programming approach presented by Byrd, Gould, Nocedal and Waltz (Math. Programming 100(1):27{48, 2004). Every iteration requires the solution of two trust-region subproblems involving piecewise linear and quadratic models, respectively. It is shown that, for a xed penalty parameter, the sequence of iterates approaches stationarity of the penalty function. A procedure for dynamically adjusting the penalty parameter is described, and global convergence results for it are established.
Organisation	CCLRC , ESC , CSE-NAG
Keywords
Funding Information
Related Research Object(s):	40546 , 29792
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Language	English (EN)

Type	Details	URI(s)	Local file(s)	Year
Report	RAL Technical Reports RAL-TR-2004-031. 2004.		bgnwRAL2004031.pdf	2004