Speed up decomposition

[hansemandse]

The current algorithm for decomposing matrices is rather slow. With the help of the time function included in util.scala, the source of this long processing duration has been isolated to the generateMatchings function. This function currently handles a slice row-wise and recursively selects one factor at a time to add. Perhaps there are alternative strategies to this selection that perform better? The original paper says "quantized matching pursuit," but it is not clear what exactly this refers to.

Orthogonally, we observe that the decomposition algorithm's independent treatment of slices lends itself very well to parallelization. Perhaps simply making the main function multi-threaded can give rise to sufficient performance?