Abstract
We introduce the affine OBDD model and show that zero-error affine OBDDs can be exponentially narrower than bounded-error unitary and probabilistic OBDDs on certain problems. Moreover, we show that Las Vegas unitary and probabilistic OBDDs can be quadratically narrower than deterministic OBDDs. We also obtain the same results for the automata versions of these models.
Author supplied keywords
Cite
CITATION STYLE
Ibrahimov, R., Khadiev, K., Prūsis, K., & Yakaryılmaz, A. (2018). Error-free affine, unitary, and probabilistic OBDDS. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10952 LNCS, pp. 175–187). Springer Verlag. https://doi.org/10.1007/978-3-319-94631-3_15
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
