The Minimum Curvature gridding algorithm for gradient maps solves the specified partial differential equation using a successive over-relaxation algorithm. The interior is updated using a "chessboard" strategy, as discussed in Press, et al. (1988, p. 868). The only difference is that the biharmonic equation must have nine different "colors," rather than just black and white.
The Relaxation factor is as described in Press et al. (1988). In general, the Relaxation factor should not be altered. The default value (1.0) is a good generic value. Roughly, the higher the Relaxation factor (closer to two) the faster the Minimum Curvature algorithm converges, but the more likely it will not converge at all. The lower the Relaxation factor (closer to zero) the more likely the Minimum Curvature algorithm will converge, but the algorithm is slower. The optimal Relaxation factor is derived through trial and error.
See Also