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[null,"1.","Record Nr.",null,"UNINA9910698856403321"]
[null,null,"Titolo",null,"Investment guaranties [[electronic resource] ] : Agreement between the United States of America and Sao Tome and Principe signed at Washington, September 10, 1998"]
[null,null,"Pubbl/distr/stampa",null,"[Washington, D.C.] : , : U.S. Dept. of State, , [1998?]"]
[null,null,"Descrizione fisica",null,"1 online resource (7 unnumbered pages)"]
[null,null,"Collana",null,"Treaties and other international acts series ; ; 12985"]
[null,null,"Soggetti",null,"Investments, American - Sao Tome and Principe"]
[{"$nulls":4},"Investment guaranty insurance - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from title screen (viewed on July 28, 2009)."]
[null,"2.","Record Nr.",null,"UNINA9910726283603321"]
[null,null,"Titolo",null,"Integration of Constraint Programming, Artificial Intelligence, and Operations Research : 20th International Conference, CPAIOR 2023, Nice, France, May 29 –June 1, 2023, Proceedings / / edited by Andre A. Cire"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"9783031332715"]
[{"$nulls":4},"9783031332708"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (522 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13884"]
[null,null,"Disciplina",null,"004.0151"]
[null,null,"Soggetti",null,"Computer science - Mathematics"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Mathematics of Computing"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Computer Engineering and Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- 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 -- "]
[{"$nulls":4},"8 Related Work -- 9 Conclusion -- References -- Column Elimination for Capacitated Vehicle Routing Problems -- 1 Introduction -- 2 Column Formulation for CVRP -- 3 Decision Diagram Formulation for CVRP -- 3.1 From Dynamic Programming to Decision Diagrams -- 3.2 Dynamic Programming for Route Relaxations -- 3.3 Exact and Relaxed Decision Diagrams -- 3.4 Constrained Network Flow Formulation -- 4 Column Elimination Procedure -- 5 Lagrangian Relaxation -- 6 Cutting Planes -- 7 Reduced Cost-Based Arc Fixing -- 8 Experimental Results -- 9 Conclusion -- References -- Cutting Plane Selection with Analytic Centers and Multiregression -- 1 Introduction -- 2 Related Work -- 3 Contributions and Methodology -- 3.1 Analytic Center-Based Methods -- 3.2 Multiple LP Solutions -- 3.3 Properties and Limitations of the Distance Measures -- 3.4 Multi-output Regression -- 4 Experiments -- 4.1 Root Node Results -- 4.2 Branch and Bound Generalisation -- 4.3 Regression Model Results."]
[{"$nulls":4},"5 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."]
[{"$nulls":4},"2 Preliminaries and Notation -- 3 Graph Invariants and Their Induced Graph Orderings -- 4 Symmetry Breaking Constraints with Graph Invariants -- 5 An Application: Generation of Cubic Graphs -- 6 Conclusion -- References -- Optimization Bounds from Decision Diagrams in Haddock -- 1 Introduction -- 2 Background -- 2.1 MDD as Layered Transition System -- 2.2 State Properties -- 2.3 Transition Functions -- 2.4 Transition Existence Function -- 2.5 Node Relaxation Functions -- 2.6 MDD Language -- 3 MDDs for Optimization -- 4 Restricted Decision Diagrams -- 4.1 Restricted MDDs in Haddock Propagation -- 4.2 Relaxed MDDs in Haddock Propagation -- 4.3 Restricted MDDs and Constraints External to the MDD -- 5 Best-First Search -- 6 Empirical Evaluation -- 7 Conclusion -- References -- ZDD-Based Algorithmic Framework for Solving Shortest Reconfiguration Problems -- 1 Introduction -- 2 Preliminaries -- 2.1 Reconfiguration Problems -- 2.2 Zero-Suppressed Decision Diagram (ZDD) -- 3 ZDD-"]
[{"$nulls":4},"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."]
[{"$nulls":4},"Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions -- 1 Introduction -- 2 Notation -- 3 The Linear Regions of Pruned Neural Networks -- 4 Pruning Based on Linear Regions -- 5 Counting Linear Regions in Subspaces -- 6 Computational Experiments -- 7 Conclusion -- References -- OAMIP: Optimizing ANN Architectures Using Mixed-Integer Programming -- 1 Introduction -- 1.1 Related Work -- 2 Preliminaries -- 3 Neuron Importance Score -- 3.1 MIP Constraints -- 3.2 Bound Propagation -- 3.3 MIP Objective -- 4 OAMIP: Pruning Approach -- 5 Empirical Results -- 5.1 OAMIP Robustness -- 5.2 Comparison to Random and Critical Pruning -- 5.3 Generalization Between Different Datasets -- 5.4 Comparison to SNIP -- 6 Discussion -- References -- Predicting the Optimal Period for Cyclic Hoist Scheduling Problems -- 1 Introduction -- 2 Hoist Scheduling Problem -- 3 Methodology -- 3.1 Data -- 3.2 ML Model Training -- 4 Experimental Results -- 4.1 Experiment 1: ML Predictive Power and Model Selection -- 4.2 Experiment 2: Bounds and Solutions -- 4.3 Experiment 3: Solver Performance with Predicted Bounds -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Scalable and Near-Optimal -Tube Clusterwise Regression -- 1 Introduction -- 2 Related Work -- 3 Optimal CLR with -Tube Objective -- 3.1 Reduction of -Tube CLR to a MILP -- 3.2 Row Generation Methodology -- 4 Empirical Evaluation -- 4.1 Synthetic Dataset Experiments -- 4.2 Real Dataset Experiments -- 5 Conclusion -- References -- Branch &amp -- Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints -- 1 Introduction -- 2 Background -- 3 Branch &amp -- Learn with Post-hoc Correction -- 4 Case Studies -- 4.1 Maximum Flow with Unknown Edge Capacities -- 4.2 0-1 Knapsack with Unknown Weights."]
[{"$nulls":4},"4.3 Minimum Cost Vertex Cover with Unknown Costs and Edge Values -- 5 Experimental Evaluation -- 5.1 B&amp -- 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 "]
[{"$nulls":4},"Evaluation Metrics -- 4.5 Numerical Results -- 4.6 Post-Mortem Analysis -- 5 Related Work -- 6 Conclusions -- References -- Exploiting Entropy in Constraint Programming -- 1 Introduction -- 2 Belief Propagation for CSPs -- 3 Accuracy of BP-Estimated Marginals and Entropy -- 4 Exploiting Entropy -- 4.1 Deciding When to Use BP -- 4.2 Deciding When to Stop BP Iterations -- 4.3 Deciding When to Activate Damping -- 4.4 Branching to Search for a Solution -- 5 Experimental Evaluation -- 5.1 Experimental Protocol -- 5.2 Evaluation -- 6 Conclusion -- References -- Constraint Propagation on GPU: A Case Study for the Cumulative Constraint -- 1 Introduction -- 2 Background -- 2.1 Constraint Satisfaction/Optimization Problem -- 2.2 Cumulative."]
[{"$nulls":4},"2.3 GPUs and CUDA."]
[null,null,null,"Sommario/riassunto","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."]