DoubleLex Revisited and Beyond
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摘要The paper proposes Maximum Residue (MR) as a notion to evaluate the strength of a symmetry breaking method. We give a proof to improve the best known DoubleLex MR upper bound from m!n! - (m!+n!) to min(m!,n!) for an m x n matrix model. Our result implies that DoubleLex works well on matrix models where min(m, n) is relatively small. We further study the MR bounds of SwapNext and SwapAny, which are extensions to DoubleLex breaking further a small number of composition symmetries. Such theoretical comparisons suggest general principles on selecting Lex-based symmetry breaking methods based on the dimensions of the matrix models. Our experiments confirm the theoretical predictions as well as efficiency of these methods.
出版社接受日期10.05.2019
著者Xuming Huang, Jimmy Lee
會議名稱The 28th International Joint Conference on Artificial Intelligence
會議開始日10.08.2019
會議完結日16.08.2019
會議地點Macao, China
會議國家/地區澳門
會議論文集題名Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
出版年份2019
月份8
出版社International Joint Conferences on Artificial Intelligence
頁次1101 - 1107
電子國際標準書號978-0-9992411-4-1
語言美式英語
關鍵詞Constraint Satisfaction, Formulation, Symmetry Breaking

上次更新時間 2020-24-11 於 15:20