Greedy Algorithms for Optimal Distribution Approximation

Abstract

The approximation of a discrete probability distribution t by an M-type distribution p is considered. The approximation error is measured by the informational divergence D ( t ∥ p ) , which is an appropriate measure, e.g., in the context of data compression. Properties of the optimal approximation are derived and bounds on the approximation error are presented, which are asymptotically tight. A greedy algorithm is proposed that solves this M-type approximation problem optimally. Finally, it is shown that different instantiations of this algorithm minimize the informational divergence D ( p ∥ t ) or the variational distance ∥ p − t ∥ 1