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...
| Autores: | , |
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| Formato: | informe técnico |
| Fecha de publicación: | 1995 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/82261 |
| Acesso em linha: | https://hdl.handle.net/2117/82261 |
| Access Level: | acceso abierto |
| Palavra-chave: | Obdds Ordered binary decision diagrams Àrees temàtiques de la UPC::Informàtica |
| Resumo: | 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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