Compressing combinatorial objects

Most of the world’s digital data is currently encoded in a sequential form, and compression methods for sequences have been studied extensively. However, there are many types of nonsequential data for which good compression techniques are still largely unexplored. This paper contributes insights and concrete techniques for compressing various kinds of nonsequential data via arithmetic coding, and derives re-usable probabilistic data models from fairly generic structural assumptions. Near-optimal compression methods are described for certain types of permutations, combinations and multisets; and the conditions for optimality are made explicit for each method.

• Type: Journal
• Year: 2016
• Where: CORR
• Authors: Christian Steinruecken
• Keywords: CORR 2016, Education