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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/7443
Title: Variable Metric Methods for Minimizing a Class of Nondifferentiable Functions
Authors: Han, Shih-Ping
Keywords: computer science
technical report
Issue Date: Sep-1977
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR77-322
Abstract: We develop a class of methods for minimizing a nondifferentiable function which is the maximum of a finite number of smooth functions. The methods proceed by solving iteratively qquadratic programming problems to generate search directions. For efficiency the matrices in the quadratic programming problems are suggested to be updated in a variable metric way. By doing so, the methods possess many attractive features of variable metric methods and can be viewed as their natural extension to the nondifferentiable case. To avoid the difficulties of an exact line search, a practical stepsize procedure is also introduced. Under mild asumptions the resulting method converge globally.
URI: http://hdl.handle.net/1813/7443
Appears in Collections:Computer Science Technical Reports

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