The routine M1QN3 has been designed to minimize functions depending on a very large number of variables (several hundred million is sometimes possible) not subject to constraints. It implements a limited memory quasi-Newton technique (the L-BFGS method of J. Nocedal) with a dynamically updated scalar or diagonal preconditioner. It uses line-search to enforce global convergence; more precisely, the step-size is determined by the Fletcher-LemarÃÂ©chal algorithm and realizes the Wolfe conditions.
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|GPLv3||Kelly Hopkins||14 January 2009|
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