Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization

This article presents two variants of memoryless quasi-Newton methods with backtracking line search for large-scale unconstrained minimization. These updating methods are derived by means of a least-change updating strategy subjected to some weaker form of secant relation obtained by projecting the secant equation onto the search direction. In such a setting, the search direction can be computed without the need of calculation and storage of matrices. We establish the convergence properties for these methods, and their performance is tested on a large set of test functions by comparing with standard methods of similar computational cost and storage requirement. Our numerical results indicate that significant improvement has been achieved with respect to iteration counts and number of function evaluations.

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