Instance-specific algorithm configuration via unsupervised deep graph clustering
Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple num...
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sg-smu-ink.sis_research-90892023-09-07T07:29:27Z Instance-specific algorithm configuration via unsupervised deep graph clustering SONG, Wen LIU, Yi CAO, Zhiguang WU, Yaoxin LI, Qiqiang Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense instance embeddings unsupervisedly, which facilitates clustering heterogeneous instances into homogeneous clusters for training instance-specific configurations. Experimental results on multiple benchmarks show that the proposed method can improve the solving efficiency of CPLEX on highly heterogeneous instances, and outperform existing instance specific AC methods. 2023-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8086 info:doi/10.1016/j.engappai.2023.106740 https://ink.library.smu.edu.sg/context/sis_research/article/9089/viewcontent/1_s2.0_S0952197623009247_pvoa_cc_by.pdf http://creativecommons.org/licenses/by/3.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Algorithm configuration Unsupervised graph embedding Mixed-integer programming Artificial Intelligence and Robotics Theory and Algorithms |
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Algorithm configuration Unsupervised graph embedding Mixed-integer programming Artificial Intelligence and Robotics Theory and Algorithms SONG, Wen LIU, Yi CAO, Zhiguang WU, Yaoxin LI, Qiqiang Instance-specific algorithm configuration via unsupervised deep graph clustering |
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Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense instance embeddings unsupervisedly, which facilitates clustering heterogeneous instances into homogeneous clusters for training instance-specific configurations. Experimental results on multiple benchmarks show that the proposed method can improve the solving efficiency of CPLEX on highly heterogeneous instances, and outperform existing instance specific AC methods. |
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text |
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SONG, Wen LIU, Yi CAO, Zhiguang WU, Yaoxin LI, Qiqiang |
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SONG, Wen LIU, Yi CAO, Zhiguang WU, Yaoxin LI, Qiqiang |
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SONG, Wen |
title |
Instance-specific algorithm configuration via unsupervised deep graph clustering |
title_short |
Instance-specific algorithm configuration via unsupervised deep graph clustering |
title_full |
Instance-specific algorithm configuration via unsupervised deep graph clustering |
title_fullStr |
Instance-specific algorithm configuration via unsupervised deep graph clustering |
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Instance-specific algorithm configuration via unsupervised deep graph clustering |
title_sort |
instance-specific algorithm configuration via unsupervised deep graph clustering |
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Institutional Knowledge at Singapore Management University |
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2023 |
url |
https://ink.library.smu.edu.sg/sis_research/8086 https://ink.library.smu.edu.sg/context/sis_research/article/9089/viewcontent/1_s2.0_S0952197623009247_pvoa_cc_by.pdf |
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