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

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書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引頻次




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書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research讀者反饋學(xué)科排名

Congeal

DIKE

,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.

天然熱噴泉

,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 ..

通情達(dá)理

鉆孔

,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

,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

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

淡紫色花

,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