Regularization of Limited Memory Quasi-Newton Methods for Large-Scale Nonconvex Minimization
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This paper deals with the unconstrained optimization of smooth objective functions. It presents a class of regularized quasi-Newton methods whose globalization turns out to be more efficient than standard line search or trust-region strategies. The focus is therefore on the solution of large-scale problems using limited memory quasi-Newton techniques. Global convergence of the regularization methods is shown under mild assumptions. The details of the regularized limited memory quasi-Newton updates are discussed including their compact representations. Numerical results using all large-scale test problems from the CUTEst collection indicate that the regularization method outperforms the standard line search limited memory BFGS method.
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