Elsevier

Information and Computation

Volume 201, Issue 2, 15 September 2005, Pages 160-177
Information and Computation

On the influence of the variable ordering for algorithmic learning using OBDDs

https://doi.org/10.1016/j.ic.2005.05.004Get rights and content
Under an Elsevier user license
open archive

Abstract

OBDDs with a fixed variable ordering are used successfully as data structure in experiments with learning heuristics based on examples. In this paper, it is shown that, for some functions, it is necessary to develop an algorithm to learn also a good OBDD variable ordering. There are functions with the following properties. They have OBDDs of linear size for optimal variable orderings. But for all but a small fraction of all variable orderings one needs large size to represent a list of randomly chosen examples. These properties are shown for simple functions like the multiplexer and the inner product.

Cited by (0)

1

This work was supported by DFG Grant Kr 1521/3-1.

2

The research was partially supported by GA of the Czech Republic, Grant No. 201/98/0717.

3

This work was supported by DFG Grant We 1066/8-1 and by the DFG as part of the Collaborative Research Center “Computational Intelligence” (531).