使委屈 发表于 2025-3-21 18:59:34

书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research影响因子(影响力)<br>        http://figure.impactfactor.cn/if/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research影响因子(影响力)学科排名<br>        http://figure.impactfactor.cn/ifr/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research网络公开度<br>        http://figure.impactfactor.cn/at/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research网络公开度学科排名<br>        http://figure.impactfactor.cn/atr/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引频次<br>        http://figure.impactfactor.cn/tc/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引频次学科排名<br>        http://figure.impactfactor.cn/tcr/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research年度引用<br>        http://figure.impactfactor.cn/ii/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research年度引用学科排名<br>        http://figure.impactfactor.cn/iir/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research读者反馈<br>        http://figure.impactfactor.cn/5y/?ISSN=BK0468833<br><br>        <br><br>书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research读者反馈学科排名<br>        http://figure.impactfactor.cn/5yr/?ISSN=BK0468833<br><br>        <br><br>

Congeal 发表于 2025-3-21 23:36:21

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DIKE 发表于 2025-3-22 02:39:06

,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.

天然热喷泉 发表于 2025-3-22 07:08:52

,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 ..

通情达理 发表于 2025-3-22 11:29:42

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钻孔 发表于 2025-3-22 15:58:47

,ZDD-Based Algorithmic Framework for Solving Shortest Reconfiguration Problems,inds a shortest transformation between two given feasible solutions if such a transformation exists. Moreover, the proposed framework provides rich information on the solution space, such as its connectivity and all feasible solutions that are reachable from a specified one. Finally, we demonstrate

Ganglion 发表于 2025-3-22 17:21:13

,Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?,ts can significantly enhance performance. However, contrary to initial expectations, we also observed that heatmaps did not present significant advantages over simpler distance measures for these purposes. Therefore, we faced a common —though rarely documented— situation of overkill: GNNs can indeed

guardianship 发表于 2025-3-22 21:19:41

OAMIP: Optimizing ANN Architectures Using Mixed-Integer Programming,s well on a single dataset but also generalizes across multiple ones upon retraining of network weights. Additionally, we present a scalable implementation of our pruning methodology by decoupling the importance scores across layers using auxiliary networks. Finally, we validate our approach experim

Painstaking 发表于 2025-3-23 03:39:34

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淡紫色花 发表于 2025-3-23 07:20:47

,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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