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Trust-Search Methods for Unconstrained Optimization
Philip E. Gill
Department of Mathematics
UCSD
Abstract
Recent research on interior methods has
re-emphasized the role of sequential
unconstrained optimization for the
solution of nonlinear programming
problems. We focus on the numerical
linear algebra associated with a class of
"trust-search" methods that combine the
best features of line-search and
trust-region methods for unconstrained
optimization.
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