Article ID Journal Published Year Pages File Type
4642137 Journal of Computational and Applied Mathematics 2007 8 Pages PDF
Abstract

In this work some interesting relations between results on basic optimization and algorithms for nonconvex functions (such as BFGS and secant methods) are pointed out. In particular, some innovative tools for improving our recent secant BFGS-type and LQNLQN algorithms are described in detail.

Keywords
Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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Low complexity secant quasi-Newton minimization algorithms for nonconvex functions
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