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Trust-region methods for large-scale unconstrained optimization
Philip E. Gill
Department of Mathematics
UCSD
Abstract
We consider methods for large-scale unconstrained optimization
based on finding an approximate solution of a quadratically
constrained trust-region subproblem. The solver is based on
sequential subspace minimization with a modified barrier
"accelerator" direction in the subspace basis.
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