Learning ordered binary decision diagrams
We study the learnability of ordered binary decision diagrams (obdds). We give a polynomial-time algorithm using membership and equivalence queries that finds the minimum obdd for the target respecting a given ordering. We also prove that both types of queries and the restriction to a given ordering...
| Authors: | ; |
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| Format: | Report |
| Publication date: | 1995 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/82261 |
| Online Access: | https://hdl.handle.net/2117/82261 |
| Access Level: | Open access |
| Keyword: | Obdds Ordered binary decision diagrams Àrees temàtiques de la UPC::Informàtica |
| Summary: | We study the learnability of ordered binary decision diagrams (obdds). We give a polynomial-time algorithm using membership and equivalence queries that finds the minimum obdd for the target respecting a given ordering. We also prove that both types of queries and the restriction to a given ordering are necessary if we want minimality in the output, unless P=NP. If learning has to occur with respect to the optimal variable ordering, polynomial-time learnability implies the approximability of two NP-hard optimization problems: the problem of finding the optimal variable ordering for a given obdd and the Optimal Linear Arrangement problem on graphs. |
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