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• W2137979856 abstract "Computational experience with several limited-memory quasi-Newton and truncated Newton methods for unconstrained nonlinear optimization is described. Comparative tests were conducted on a well-known test library [J. J. Moré, B. S. Gaxbow, and K. E. Hillstrom, ACM Trans. Math. Software, 7 (1981), pp. 17–41], on several synthetic problems allowing control of the clustering of eigenvalues in the Hessian spectrum, and on some large-scale problems in oceanography and meteorology. The results indicate that among the tested limited-memory quasi-Newton methods, the L-BFGS method [D. C. Liu and J. Nocedal, Math. Programming, 45 (1989), pp. 503–528] has the best overall performance for the problems examined. The numerical performance of two truncated Newton methods, differing in the inner-loop solution for the search vector, is competitive with that of L-BFGS." @default.
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• W2137979856 date "1993-08-01" @default.
• W2137979856 modified "2023-10-14" @default.
• W2137979856 title "Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods" @default.
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• W2137979856 doi "https://doi.org/10.1137/0803029" @default.
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