Solving LP using random projections

A celebrated result of Johnson and Lindenstrauss asserts that, in high enough dimensional spaces, Euclidean distances defined by a finite set of points are approximately preserved when these points are projected to a certain lower dimensional space. We show that the distance from a point to a convex set is another approximate invariant, and leverage this result to approximately solve linear programs with a logarithmic number of rows.