Random treatment of problem solutions has proved to provide a convenient approach in surveying landscapes for optimization problems where the solutions space is vast and appears to follows no predetermined schedule or route. 

In cases where the patterns are sparsely distributed, the computational search time for the initial space can be dramatically reduced if the choice of   is chosen appropriately.


The algorithm developed above represents a canonical formulation of a clustering technique; however, it can also be used as a preconditioning search algorithm regardless of the dimensionality of the search space.

For this reason, the orthogonal search may be very effective for detecting patterns, rather than clusters.