Presentation 1998/7/23
NP-Completeness of Identifying Minimum OBDD for Monotone Functions
Yasuhiko TAKENAGA,
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Abstract(in English) An ordered binary decision diagram(OBDD) is a graph representation of a Boolean function. We consider minimum OBDD identification problems: given positive and negative examples of a Boolean function, identify the OBDD with minimum number of nodes(or with minimum width) that is consistant with all the examples. We prove in this paper that the problems are NP-complete even if we restrict the functions to monotone functions. The result implies that f(n)-width OBDD and f(n)-node OBDD of monotone functions are not learnable for some fixed f(n) under the PAC-learning model unless NP = RP.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) ordered binary decision diagram(OBDD) / PAC learning / NP-completeness / monotone function / Boolean function
Paper # COMP98-29
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Committee COMP
Conference Date 1998/7/23(1days)
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Registration To Theoretical Foundations of Computing (COMP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) NP-Completeness of Identifying Minimum OBDD for Monotone Functions
Sub Title (in English)
Keyword(1) ordered binary decision diagram(OBDD)
Keyword(2) PAC learning
Keyword(3) NP-completeness
Keyword(4) monotone function
Keyword(5) Boolean function
1st Author's Name Yasuhiko TAKENAGA
1st Author's Affiliation Department of Computer Science and Information Mathematics, University of Electro-Communications()
Date 1998/7/23
Paper # COMP98-29
Volume (vol) vol.98
Number (no) 186
Page pp.pp.-
#Pages 8
Date of Issue