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.