Many proofs in discrete mathematics and theoretical computer science are based on the probabilistic method. To prove the existence of a good object, we pick a random object and show that it is bad with low probability. This method is effective, but the underlying probabilistic machinery can be daunting. “Encoding arguments” provide an alternative presentation in which probabilistic reasoning is encapsulated in a “uniform encoding lemma.” This lemma provides an upper bound on the probability of an event using the fact that a uniformly random choice from a set of size n cannot be encoded with fewer than log2 n bits on average. With the lemma, the argument reduces to devising an encoding where bad objects have short codewords. In this expository article, we describe the basic method and provide a simple tutorial on how to use it. After that, we survey many applications to classic problems from discrete mathematics and computer science. We also give a generalization for the case of non-uniform distributions, as well as a rigorous justification for the use of non-integer codeword lengths in encoding arguments. These latter two results allow encoding arguments to be applied more widely and to produce tighter results.

Analysis of algorithms, Concentration inequalities, Encoding arguments, Entropy, Expanders, Hash tables, Incompressibility, Percolation theory, Random graphs
ACM Computing Surveys
School of Computer Science

Morin, P, Mulzer, W. (Wolfgang), & Reddad, T. (Tommy). (2017). Encoding Arguments. ACM Computing Surveys, 50(3). doi:10.1145/3084288