DAMP damp [1] limse [15]damp is usually left at the default value unless there is severe correlation, e.g. when trying to refine a pseudo-centrosymmetric structure, or refining with few data per parameter (e.g. from powder data). A value in the range 1-10000 might then be appropriate. The diagonal elements of the least-squares matrix are multiplied by (1+damp/1000) before inversion; this is a version of the Marquardt algorithm (J. Soc. Ind. Appl. Math., 11 (1963) 431-441). A side-effect of damping is that the standard deviations of poorly determined parameters will be artificially reduced; it is recommended that a final least-squares cycle be performed with little or no damping in order to improve these estimated standard deviations. Theoretically, damping only serves to improve the convergence properties of the refinement, and can be gradually reduced as the refinement converges; it should not influence the final parameter values. However in practice damping also deals effectively with rounding error problems in the (single-precision) least-squares matrix algebra, which can present problems when the number of parameters is large and/or restraints are used (especially when the latter have small esd's), and so it may not prove possible to lift the damping entirely even for a well converged refinement.
If the maximum shift/esd (excluding the overall scale factor) is greater than limse, all the shifts are scaled down by the same numerical factor so that the maximum is equal to limse. If the maximum shift/esd is smaller than limse no action is taken. This helps to prevent excessive shifts in the early stages of refinement.
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