Skip to main content


eCommons@Cornell

eCommons@Cornell >
College of Engineering >
Computer Science >
Computer Science Technical Reports >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/6164
Title: On The Convergence of Reflective Newton Methods for Large-Scale Nonlinear Minimization Subject to Bounds
Authors: Coleman, Thomas F.
Li, Yuying
Keywords: computer science
technical report
Issue Date: Nov-1992
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR92-1314
Abstract: We consider a new algorithm, a reflective Newton method, for the problem of minimizing a smooth nonlinear function of many variables, subject to upper and/or lower bounds on some of the variables. This approach generates strictly feasible iterates by following piecewise linear paths ("reflection" paths) to generate improved iterates. The reflective Newton approach does not require identification of an "activity set". In this report we establish that the reflective Newton approach is globally and quadratically convergent. Moreover, we develop a specific example of this general reflective path approach suitable for large-scale and sparse problems.
URI: http://hdl.handle.net/1813/6164
Appears in Collections:Computer Science Technical Reports

Files in This Item:

File Description SizeFormat
92-1314.pdf3.02 MBAdobe PDFView/Open
92-1314.ps627.15 kBPostscriptView/Open

Refworks Export

Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2014 Cornell University Library Contact Us