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Maximal-Sum submatrix search using a hybrid contraint programming/linear programming approach
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DOI：10.1016/j.ejor.2021.06.008.png)
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A Maximal-Sum Submatrix (MSS) maximizes the sum of the entries corresponding to the Cartesian product of a subset of rows and columns from an original matrix (with positive and negative entries). Despite being NP-hard, this recently introduced problem was already proven to be useful for practical data mining applications. It was used for identifying bi-clusters in gene expression data or to extract a sub matrix that is then visualized in a circular plot. The state-of-the-art results for MSS are obtained using an advanced Constraint Programing approach that combines a custom filtering algorithm with a Large Neighborhood Search. We improve the state-of-the-art approach by introducing new upper bounds based on linear and mixed-integer programming formulations, along with dedicated pruning algorithms. We experiment on both synthetic and real-life data, and show that our approach outperforms the previous methods. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Combinatorial optimization
Maximum-sum submatrix
Linear relaxation
Constraint programming
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6
论文数:
2.2W
被引数:
6.4W
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