Tables for
Volume F
Crystallography of biological macromolecules
Edited by M. G. Rossman and E. Arnold

International Tables for Crystallography (2006). Vol. F, ch. 18.1, p. 370   | 1 | 2 |

Section 18.1.5. Optimization

L. F. Ten Eycka* and K. D. Watenpaughb

aSan Diego Supercomputer Center 0505, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0505, USA, and bStructural, Analytical and Medicinal Chemistry, Pharmacia & Upjohn, Inc., Kalamazoo, MI 49001-0119, USA
Correspondence e-mail:

18.1.5. Optimization

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Once the choice of criteria for agreement has been made, the next step is to adjust the parameters of the model to minimize the disagreement (or maximize the agreement) between the model and the data. The literature on optimization in numerical analysis and operations research, discussed in IT C Chapters 8.1[link] –8.5[link] , is very rich. The methods can be characterized by their use of gradient information (no gradients, first derivatives, or second derivatives), by their search strategy (none, downhill, random, annealed, or a combination of these), and by various performance measures on different classes of problems. These will be discussed more fully in Section 18.1.8[link].

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