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• W2151013142 abstract "Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large-scale unconstrained optimization that interlace iterations of the limited-memory BFGS method (L-BFGS) and the Hessian-free Newton method (Computat Opt Appl 2002, 21, 143-154). We test the performance of this approach as compared to those of the L-BFGS algorithm of Liu and Nocedal and the truncated Newton (TN) with automatic preconditioner of Nash, as applied to the protein bovine pancreatic trypsin inhibitor (BPTI) and a loop of the protein ribonuclease A. These systems are described by the all-atom AMBER force field with a dielectric constant epsilon = 1 and a distance-dependent dielectric function epsilon = 2r, where r is the distance between two atoms. It is shown that for the optimal parameters the hybrid approach is typically two times more efficient in terms of CPU time and function/gradient calculations than the two other methods. The advantage of the hybrid approach increases as the electrostatic interactions become stronger, that is, in going from epsilon = 2r to epsilon = 1, which leads to a more rugged and probably more nonlinear potential energy surface. However, no general rule that defines the optimal parameters has been found and their determination requires a relatively large number of trial-and-error calculations for each problem." @default.
• W2151013142 created "2016-06-24" @default.
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• W2151013142 date "2003-06-09" @default.
• W2151013142 modified "2023-10-16" @default.
• W2151013142 title "Performance of hybrid methods for large-scale unconstrained optimization as applied to models of proteins" @default.
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• W2151013142 doi "https://doi.org/10.1002/jcc.10275" @default.
• W2151013142 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/12820130" @default.
• W2151013142 hasPublicationYear "2003" @default.
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