LEADER 01260nas 2200409 c 450 001 9910894311603321 005 20251012104132.0 011 $a2733-4392 024 7 $a10.14772/CSCCK. 035 $a(DE-599)ZDB3120459-4 035 $a(OCoLC)1321947812 035 $a(DE-101)125856534X 035 $a(CKB)3390000000030035 035 $a(EXLCZ)993390000000030035 100 $a20220527a20099999 |y | 101 0 $achi 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHa?nzi? to?ngba?o$e= ˜Theœ journal of chinese character studies (JCCS) 210 31$aBusan$cCenter for the Study of Chinese Characters in Korea, Kyungsung University$d2009- 215 $aOnline-Ressource 300 $aErscheint dreimal im Jahr 311 08$a2093-6133 517 1 $a˜Theœ journal of chinese character studies (JCCS) 517 3 $aJCCS 517 1 $ahanjayeongu 608 $aZeitschrift$2gnd-content 712 02$aKyungsung University$bCenter for the Study of Chinese Characters in Korea$4isb 801 0$b0029 801 1$bDE-101 801 2$b9999 906 $aJOURNAL 912 $a9910894311603321 996 $aHa?nzi? to?ngba?o$94439293 997 $aUNINA LEADER 12518nam 22007335 450 001 9910726283603321 005 20251225203525.0 010 $a9783031332715$b(electronic bk.) 010 $z9783031332708 024 7 $a10.1007/978-3-031-33271-5 035 $a(MiAaPQ)EBC30549261 035 $a(Au-PeEL)EBL30549261 035 $a(OCoLC)1380721651 035 $a(DE-He213)978-3-031-33271-5 035 $a(BIP)090943963 035 $a(PPN)270612327 035 $a(CKB)26742398100041 035 $a(EXLCZ)9926742398100041 100 $a20230522d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntegration of Constraint Programming, Artificial Intelligence, and Operations Research $e20th International Conference, CPAIOR 2023, Nice, France, May 29 ?June 1, 2023, Proceedings /$fedited by Andre A. Cire 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (522 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13884 311 08$aPrint version: Cire, Andre A. Integration of Constraint Programming, Artificial Intelligence, and Operations Research Cham : Springer,c2023 9783031332708 327 $aIntro -- Preface -- Organization -- Contents -- Efficiently Approximating High-Dimensional Pareto Frontiers for Tree-Structured Networks Using Expansion and Compression -- 1 Introduction -- 2 Problem Formulation -- 3 The Expansion Method -- 4 The Compression Method -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Evaluation Method -- 5.3 Experimental Results -- 5.4 Ablation Study -- 6 Conclusion -- References -- Objective-Based Counterfactual Explanations for Linear Discrete Optimization -- 1 Introduction -- 2 Background -- 2.1 Counterfactual Explanations -- 2.2 Nearest Counterfactual Explanations -- 2.3 Inverse Combinatorial Optimization -- 3 Problem Definition -- 3.1 Existence of an Explanation -- 4 The NCXplain Algorithm -- 5 Experimental Method -- 5.1 Forward Problems -- 5.2 NCEMILP Instances -- 5.3 Computational Details -- 6 Experimental Results -- 7 Limitations and Future Work -- 8 Related Work -- 9 Conclusion -- References -- Column Elimination for Capacitated Vehicle Routing Problems -- 1 Introduction -- 2 Column Formulation for CVRP -- 3 Decision Diagram Formulation for CVRP -- 3.1 From Dynamic Programming to Decision Diagrams -- 3.2 Dynamic Programming for Route Relaxations -- 3.3 Exact and Relaxed Decision Diagrams -- 3.4 Constrained Network Flow Formulation -- 4 Column Elimination Procedure -- 5 Lagrangian Relaxation -- 6 Cutting Planes -- 7 Reduced Cost-Based Arc Fixing -- 8 Experimental Results -- 9 Conclusion -- References -- Cutting Plane Selection with Analytic Centers and Multiregression -- 1 Introduction -- 2 Related Work -- 3 Contributions and Methodology -- 3.1 Analytic Center-Based Methods -- 3.2 Multiple LP Solutions -- 3.3 Properties and Limitations of the Distance Measures -- 3.4 Multi-output Regression -- 4 Experiments -- 4.1 Root Node Results -- 4.2 Branch and Bound Generalisation -- 4.3 Regression Model Results. 327 $a5 Conclusion -- References -- Handling Symmetries in Mixed-Integer Semidefinite Programs -- 1 Introduction -- 2 Computing Symmetries -- 3 Symmetry Detection -- 4 Computational Results -- References -- A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination -- 1 Introduction -- 2 Reducing a DBLP to a MILP: A Worked Example -- 3 Symbolic Calculus with Case Representation -- 3.1 Case Representation -- 3.2 Basic Case Operators -- 4 Symbolic Reduction of a DBLP to a MILP -- 4.1 Symbolic Minimization of Linear Piecewise Linear Functions -- 4.2 Symbolic Minimization of Disjointly Linear Piecewise Bilinear Functions -- 5 Empirical Analysis -- 6 Conclusion and Future Work -- References -- Local Branching Relaxation Heuristics for Integer Linear Programs -- 1 Introduction -- 2 Background -- 2.1 ILP and Its LP Relaxation -- 2.2 LNS for ILP Solving -- 2.3 LB Heuristic -- 3 Related Work -- 3.1 LNS for ILPs -- 3.2 LNS-Based Primal Heuristics in BnB -- 3.3 LNS for Other COPs -- 4 The Local Branching Relaxation Heuristic -- 5 Empirical Evaluation -- 5.1 Setup -- 5.2 Results -- 6 Conclusion -- References -- Online Learning for Scheduling MIP Heuristics -- 1 Introduction -- 2 Background -- 3 Scheduling Primal Heuristics Online -- 3.1 The Online Scheduling Framework -- 3.2 Choosing a Reward Function -- 3.3 Choosing a Bandit Algorithm -- 4 Computational Results -- References -- Contextual Robust Optimisation with Uncertainty Quantification -- 1 Introduction -- 2 Robust Predict-then-Optimise -- 3 Predictive Models with Uncertainty Quantification -- 4 Conditional Ambiguity Sets -- 5 Data-driven Robustness Parameter Specification -- 6 Computational Evaluation and Discussion -- 6.1 Simulated Problem -- References -- Breaking Symmetries with High Dimensional Graph Invariants and Their Combination -- 1 Introduction. 327 $a2 Preliminaries and Notation -- 3 Graph Invariants and Their Induced Graph Orderings -- 4 Symmetry Breaking Constraints with Graph Invariants -- 5 An Application: Generation of Cubic Graphs -- 6 Conclusion -- References -- Optimization Bounds from Decision Diagrams in Haddock -- 1 Introduction -- 2 Background -- 2.1 MDD as Layered Transition System -- 2.2 State Properties -- 2.3 Transition Functions -- 2.4 Transition Existence Function -- 2.5 Node Relaxation Functions -- 2.6 MDD Language -- 3 MDDs for Optimization -- 4 Restricted Decision Diagrams -- 4.1 Restricted MDDs in Haddock Propagation -- 4.2 Relaxed MDDs in Haddock Propagation -- 4.3 Restricted MDDs and Constraints External to the MDD -- 5 Best-First Search -- 6 Empirical Evaluation -- 7 Conclusion -- References -- ZDD-Based Algorithmic Framework for Solving Shortest Reconfiguration Problems -- 1 Introduction -- 2 Preliminaries -- 2.1 Reconfiguration Problems -- 2.2 Zero-Suppressed Decision Diagram (ZDD) -- 3 ZDD-Based Algorithmic Framework -- 3.1 Algorithmic Framework -- 3.2 Removal and Addition Operations -- 4 Versatility of Proposed Algorithm -- 4.1 Shortest, Farthest, and Connectivity Variants -- 4.2 Token Jumping Model -- 4.3 Reconfiguration Objects and Constraints -- 5 Experimental Results -- 6 Conclusion -- References -- Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill? -- 1 Introduction -- 2 Methodology -- 2.1 Hybrid Genetic Search -- 2.2 Local Search Using Relatedness Measures -- 2.3 Crossover Using Relatedness Measures -- 3 Experimental Analyses -- 3.1 Computational Environment -- 3.2 Benchmark Instances -- 3.3 Parametrization and Training of the GNN -- 3.4 Calibration of the Local Search -- 3.5 Experimental Results - Set XML -- 3.6 Experimental Results - Set X -- 4 Conclusions -- References. 327 $aGetting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions -- 1 Introduction -- 2 Notation -- 3 The Linear Regions of Pruned Neural Networks -- 4 Pruning Based on Linear Regions -- 5 Counting Linear Regions in Subspaces -- 6 Computational Experiments -- 7 Conclusion -- References -- OAMIP: Optimizing ANN Architectures Using Mixed-Integer Programming -- 1 Introduction -- 1.1 Related Work -- 2 Preliminaries -- 3 Neuron Importance Score -- 3.1 MIP Constraints -- 3.2 Bound Propagation -- 3.3 MIP Objective -- 4 OAMIP: Pruning Approach -- 5 Empirical Results -- 5.1 OAMIP Robustness -- 5.2 Comparison to Random and Critical Pruning -- 5.3 Generalization Between Different Datasets -- 5.4 Comparison to SNIP -- 6 Discussion -- References -- Predicting the Optimal Period for Cyclic Hoist Scheduling Problems -- 1 Introduction -- 2 Hoist Scheduling Problem -- 3 Methodology -- 3.1 Data -- 3.2 ML Model Training -- 4 Experimental Results -- 4.1 Experiment 1: ML Predictive Power and Model Selection -- 4.2 Experiment 2: Bounds and Solutions -- 4.3 Experiment 3: Solver Performance with Predicted Bounds -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Scalable and Near-Optimal -Tube Clusterwise Regression -- 1 Introduction -- 2 Related Work -- 3 Optimal CLR with -Tube Objective -- 3.1 Reduction of -Tube CLR to a MILP -- 3.2 Row Generation Methodology -- 4 Empirical Evaluation -- 4.1 Synthetic Dataset Experiments -- 4.2 Real Dataset Experiments -- 5 Conclusion -- References -- Branch & -- Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints -- 1 Introduction -- 2 Background -- 3 Branch & -- Learn with Post-hoc Correction -- 4 Case Studies -- 4.1 Maximum Flow with Unknown Edge Capacities -- 4.2 0-1 Knapsack with Unknown Weights. 327 $a4.3 Minimum Cost Vertex Cover with Unknown Costs and Edge Values -- 5 Experimental Evaluation -- 5.1 B& -- L-C Versus IntOpt-C -- 5.2 Post-hoc Regret on More General Problems -- 5.3 Different Combinations of Correction Functions and Penalty Functions -- 6 Conclusion -- References -- Interpretable Clustering via Soft Clustering Trees -- 1 Introduction -- 2 Soft Clustering Trees -- 2.1 Soft Decision Trees -- 2.2 Soft Clustering Trees -- 2.3 Sparsity in Soft Clustering Trees -- 2.4 Learning Sparse Soft Clustering Trees Using Continuous Optimization -- 2.5 Interpretable Spectral and Kernel-PCA Clustering -- 2.6 Scalable Training of Soft Clustering Trees -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Datasets -- 3.3 Evaluation -- 3.4 Results -- 4 Related Work -- 5 Conclusion -- References -- Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language -- 1 Introduction -- 2 Problem Description -- 3 Our Approach -- 3.1 Classical NLP -- 3.2 Modern NLP -- 3.3 Data Augmentation -- 3.4 Hybrid Modeling -- 4 Experiments -- 4.1 Ner4Opt Dataset -- 4.2 Comparisons -- 4.3 Experimental Setup -- 4.4 Evaluation Metrics -- 4.5 Numerical Results -- 4.6 Post-Mortem Analysis -- 5 Related Work -- 6 Conclusions -- References -- Exploiting Entropy in Constraint Programming -- 1 Introduction -- 2 Belief Propagation for CSPs -- 3 Accuracy of BP-Estimated Marginals and Entropy -- 4 Exploiting Entropy -- 4.1 Deciding When to Use BP -- 4.2 Deciding When to Stop BP Iterations -- 4.3 Deciding When to Activate Damping -- 4.4 Branching to Search for a Solution -- 5 Experimental Evaluation -- 5.1 Experimental Protocol -- 5.2 Evaluation -- 6 Conclusion -- References -- Constraint Propagation on GPU: A Case Study for the Cumulative Constraint -- 1 Introduction -- 2 Background -- 2.1 Constraint Satisfaction/Optimization Problem -- 2.2 Cumulative. 327 $a2.3 GPUs and CUDA. 330 $aThis book constitutes the proceedings of the 20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, held in Nice, France, during May 29?June 1, 2023. The 26 full papers and the 6 short papers presented in this book were carefully reviewed and selected from a total of 71 submissions. The content of the papers present new techniques or new applications, and provide an opportunity for researchers in one area to learn about techniques in the others. Besides they give researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13884 606 $aComputer science$xMathematics 606 $aArtificial intelligence 606 $aComputer science 606 $aComputer engineering 606 $aComputer networks 606 $aMathematics of Computing 606 $aArtificial Intelligence 606 $aTheory of Computation 606 $aComputer Engineering and Networks 615 0$aComputer science$xMathematics. 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aComputer engineering. 615 0$aComputer networks. 615 14$aMathematics of Computing. 615 24$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aComputer Engineering and Networks. 676 $a004.0151 702 7$aCire$b Andre A.$4edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910726283603321 996 $aIntegration of Constraint Programming, Artificial Intelligence, and Operations Research$92880461 997 $aUNINA