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Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 20th International C Andre A. Cire Conference proc

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發(fā)表于 2025-3-21 18:59:34 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research
副標(biāo)題20th International C
編輯Andre A. Cire
視頻videohttp://file.papertrans.cn/469/468833/468833.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 20th International C Andre A. Cire Conference proc
描述.This book constitutes the proceedings of the 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, held in Nice, France, during May 29–June 1, 2023.. The 26 full papers and the 6 short papers presented in this book were carefully reviewed and selected from a total of 71 submissions. .The content of the papers present new techniques or new applications, and provide an opportunity for researchers in one area to learn about techniques in the others. Besides they give researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems..
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; constraint programming; education; engineering algorithms; algorithms; linguisti
版次1
doihttps://doi.org/10.1007/978-3-031-33271-5
isbn_softcover978-3-031-33270-8
isbn_ebook978-3-031-33271-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
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,A Mixed-Integer Linear Programming Reduction of?Disjoint Bilinear Programs via?Symbolic Variable Elat we significantly outperform Gurobi. We also evaluate our method on a variety of synthetic instances to analyze the effects of DBLP problem size and sparsity w.r.t. MILP compilation size and solution efficiency.
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,Online Learning for?Scheduling MIP Heuristics,control two different classes of heuristics simultaneously by a single learning agent. We verify our approach numerically and show consistent node reductions over the MIPLIB 2017 Benchmark set. For harder instances that take at least 1000?s to solve, we observe a speedup of ..
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,Scalable and?Near-Optimal ,-Tube Clusterwise Regression,on solution that can optimally converge for the full dataset while only requiring optimization over a subset of the data. Our results on a variety of synthetic and benchmark real datasets show that our Clusterwise Regression MILP formulation provides near-optimal solutions up to 100,000 data points
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