Skip to Main content Skip to Navigation
Conference papers

Russian doll search with tree decomposition

Abstract : Optimization in graphical models is an important problem which has been studied in many AI frameworks such as weighted CSP, maximum satisfiability or probabilistic networks. By identifying conditionally independent subproblems, which are solved independently and whose optimum is cached, recent Branch and Bound algorithms offer better asymptotic time complexity. But the locality of bounds induced by decomposition often hampers the practical effects of this result because subproblems are often uselessly solved to optimality. Following the Russian Doll Search (RDS) algorithm, a possible approach to overcome this weakness is to (inductively) solve a relaxation of each subproblem to strengthen bounds. The algorithm obtained generalizes both RDS and treedecomposition based algorithms such as BTD or AND-OR Branch and Bound. We study its efficiency on different problems, closing a very hard frequency assignment instance which has been open for more than 10 years.
Document type :
Conference papers
Complete list of metadata

Cited literature [6 references]  Display  Hide  Download
Contributor : Migration ProdInra Connect in order to contact the contributor
Submitted on : Wednesday, June 3, 2020 - 10:20:23 PM
Last modification on : Thursday, March 17, 2022 - 1:44:02 PM
Long-term archiving on: : Friday, December 4, 2020 - 5:51:09 PM


Russian doll search with tree ...
Publisher files allowed on an open archive


  • HAL Id : hal-02755904, version 1
  • PRODINRA : 187829
  • WOS : 000283727900095



Marti Sanchez, David Allouche, Simon de Givry, Thomas Schiex. Russian doll search with tree decomposition. 21st International Joint Conference on Artificial Intelligence, Jul 2009, Pasadena, United States. pp.6. ⟨hal-02755904⟩



Record views


Files downloads