structural:scholar_edition, structural:expansion, information:classification, linguistic:voice_change, information:highlighting
Shift-cutting

If divergence occurs, a simple expedient is to reduce the length of the shift vector, Δ β {\displaystyle \Delta {\boldsymbol {\beta }}}, by a fraction, f

For example, the length of the shift vector may be successively halved until the new value of the objective function is less than its value at the last iteration. The fraction, f could be optimized by a line search. As each trial value of f requires the objective function to be re-calculated it is not worth optimizing its value too stringently.

When using shift-cutting, the direction of the shift vector remains unchanged. This limits the applicability of the method to situations where the direction of the shift vector is not very different from what it would be if the objective function were approximately quadratic in the parameters, β k. {\displaystyle {\boldsymbol {\beta }}^{k}.}