Learning to Solve QBF

By Horst Samulowitz, Roland Memisevic. 2007

100 citations recorded by the source corpus.

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Citations in the corpus

Learning Combinatorial Optimization Algorithms over Graphs (2017, methodology): “[35] Samulowitz, Horst and Memisevic, Roland.”
Learning to Run Heuristics in Tree Search (2017, methodology): “For instance, deep learning can help tune gradient descent [Andrychowicz et al., 2016], reinforcement learning is used for job-shop scheduling [Zhang and Dietterich, 1995], and classification is used for selecting an algorithm for QBF subproblems [Samulowitz and Memisevic, 2007].”
Algorithm Selection for Combinatorial Search Problems: A Survey (2012, methodology): “Smith-Miles, 2008b). In some cases, no performance models as such are used at all. Caseau, Laburthe, and Silverstein (1999), Minton (1996), Balasubramaniam et al.”
Instance-Specific Algorithm Configuration (2014, methodology): “In [81] a method for adaptive search guidance for QBF solvers was based on logistic regression.”
Hydra-MIP : Automated Algorithm Configuration and Selection for Mixed Integer Programming (2011, methodology): “There are many existing techniques for algorithm selection, based on either regression [30, 26] or classification[10, 9, 25, 23].”
Cross-disciplinary perspectives on meta-learning for algorithm selection (2009, methodology): “2005], dynamic algorithm selection [Armstrong et al. 2006; Samulowitz and Memisevic 2007], and real-time analysis of algorithms, particularly dynamic tuning of parameters via racing algorithms [Maron and Moore 1997].”
SATzilla: Portfolio-based Algorithm Selection for SAT (2008, methodology): “Similarly, Samulowitz and Memisevic (2007) employed classification to switch between different heuristics for QBF solving during the search.”
Alors: An algorithm recommender system (2017, methodology): “The CSP-2010 dataset stands out as being particularly diﬃcult, for the same reasons as in the Matrix Completion case.”