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Line Search Algorithms for Projected-Gradient Quasi-Newton Methods
Michael Ferry
Department of Mathematics
University of California, San Diego
Abstract
We briefly survey line search algorithms for unconstrained optimization.
Next, we consider the search direction and line search strategies used in
several algorithms that implement a quasi-Newton method for simple bounds,
including algorithm L-BFGS-B. In this context, we discuss two
currently-used line search algorithms and introduce a new method meant to
combine the best properties of two different strategies. We present a
modified L-BFGS-B method using the new line search and demonstrate its
significant performance gains by numerical tests using the CUTEr test set.
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