In the article How to Improve the Brightness of an LED Using a Free-Form Mirror, an example is presented of optimizing a complex mirror so that the angular distribution of an LED's output is shifted into a more desirable profile. Here is a scan of the merit function of the starting design as a function of just one variable:



It can be seen that for long regions of merit function space, there is no change in the merit function at all, and when change does come it is sudden and discontinuous. This makes optimization by gradient search techniques difficult. In the referenced article, we had to use Global Search, and also turn on optimization variables in a stepwise fashion, in order to get optimization to make meaningful progress.

Orthogonal Descent (OD) optimization uses an orthonormalization of the variables and discrete sampling of solution space to reduce the merit function. The OD algorithm does not compute numerical derivatives of the merit function. For systems with inherently noisy merit functions, such as non-sequential systems, OD will usually outperform  DLS optimization.