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Intro; Preface; Tutorials and Workshops; Organization; Invited Talks; Using Constraints in Machine Learning; Discrete Optimization and Machine Learning for Sustainability; Verification and Explanation of Deep Neural Networks; AI and Robust Optimization for Social Good; Contents; Technical Track; Instance Generation via Generator Instances; 1 Introduction; 2 Related Work; 3 Background; 4 Methodology; 4.1 Rewriting Rules; 4.2 Correctness of Instance Generation via Generator Instances; 4.3 Tuning Instance Difficulty; 4.4 Problem Classes; 4.5 Experimental Setup; 5 Results and Analysis
6 Conclusions and Future WorkReferences; Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints; 1 Introduction; 2 Preliminaries; 3 Background: AMO and ALO Detection; 4 AMO and EO Relations in Savile Row; 4.1 Mutex Inference; 4.2 Normalisation; 4.3 AMO and EO Detection; 4.4 Reformulation Example; 5 Experimental Evaluation; 5.1 Combinatorial Auctions; 5.2 MRCPSP; 5.3 NSP; 5.4 MMKP; 5.5 Experimental Results; 6 Conclusion and Future Work; References; Exploring Declarative Local-Search Neighbourhoods with Constraint Programming
1 Introduction2 Background; 2.1 MiniZinc and Declarative Neighbourhoods; 2.2 Local Search; 3 Encoding a Declarative Neighbourhood as a CP Model; 3.1 Encoding the Current and Next Valuations; 3.2 Encoding a Move; 3.3 The Neighbourhood Model and Neighbourhood Exploration; 4 Implementing a Local-Search Solver Using a CP Solver; 4.1 Implementation of the WRITES Global Constraint; 4.2 Constraint Softening Scheme; 4.3 Control Flow; 5 Experimental Evaluation; 6 Conclusion, Related Work, and Future Work; References; Vehicle Routing by Learning from Historical Solutions; 1 Introduction; 2 Related Work
3 Formalisation3.1 Standard CVRP; 3.2 CVRP with Arc Probabilities; 4 Learning Transition Probabilities from Data; 4.1 Constructing the Transition Probability Matrix; 4.2 Evaluation Schemes; 4.3 Weighing Schemes; 4.4 Adding Distance-Based Probabilities; 5 Experiments; 5.1 Numerical Results; 5.2 Parameter Sensitivity; 5.3 Detailed Example; 6 Concluding Remarks; References; On Symbolic Approaches for Computing the Matrix Permanent; 1 Introduction; 2 Preliminaries; 2.1 Algebraic Decision Diagrams; 2.2 Ryser's Formula; 3 Related Work; 4 Representing Ryser's Formula Symbolically
4.1 Implementation Details5 Experimental Methodology; 5.1 Algorithm Suite; 5.2 Experimental Setup; 5.3 Benchmarks; 6 Results; 6.1 ADD Size Vs Time Taken by RysersADD; 6.2 Performance on Dense Matrices; 6.3 Performance on Sparse Matrices; 6.4 Performance on Similar-Row Matrices; 6.5 Performance on SuiteSparse Matrix Collection; 6.6 Performance on Fullerene Adjacency Matrices; 7 Conclusion; References; Towards the Characterization of Max-Resolution Transformations of UCSs by UP-Resilience; 1 Introduction; 2 Definitions and Notations; 3 Preliminaries and Motivation; 4 Contributions; 5 Conclusion
6 Conclusions and Future WorkReferences; Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints; 1 Introduction; 2 Preliminaries; 3 Background: AMO and ALO Detection; 4 AMO and EO Relations in Savile Row; 4.1 Mutex Inference; 4.2 Normalisation; 4.3 AMO and EO Detection; 4.4 Reformulation Example; 5 Experimental Evaluation; 5.1 Combinatorial Auctions; 5.2 MRCPSP; 5.3 NSP; 5.4 MMKP; 5.5 Experimental Results; 6 Conclusion and Future Work; References; Exploring Declarative Local-Search Neighbourhoods with Constraint Programming
1 Introduction2 Background; 2.1 MiniZinc and Declarative Neighbourhoods; 2.2 Local Search; 3 Encoding a Declarative Neighbourhood as a CP Model; 3.1 Encoding the Current and Next Valuations; 3.2 Encoding a Move; 3.3 The Neighbourhood Model and Neighbourhood Exploration; 4 Implementing a Local-Search Solver Using a CP Solver; 4.1 Implementation of the WRITES Global Constraint; 4.2 Constraint Softening Scheme; 4.3 Control Flow; 5 Experimental Evaluation; 6 Conclusion, Related Work, and Future Work; References; Vehicle Routing by Learning from Historical Solutions; 1 Introduction; 2 Related Work
3 Formalisation3.1 Standard CVRP; 3.2 CVRP with Arc Probabilities; 4 Learning Transition Probabilities from Data; 4.1 Constructing the Transition Probability Matrix; 4.2 Evaluation Schemes; 4.3 Weighing Schemes; 4.4 Adding Distance-Based Probabilities; 5 Experiments; 5.1 Numerical Results; 5.2 Parameter Sensitivity; 5.3 Detailed Example; 6 Concluding Remarks; References; On Symbolic Approaches for Computing the Matrix Permanent; 1 Introduction; 2 Preliminaries; 2.1 Algebraic Decision Diagrams; 2.2 Ryser's Formula; 3 Related Work; 4 Representing Ryser's Formula Symbolically
4.1 Implementation Details5 Experimental Methodology; 5.1 Algorithm Suite; 5.2 Experimental Setup; 5.3 Benchmarks; 6 Results; 6.1 ADD Size Vs Time Taken by RysersADD; 6.2 Performance on Dense Matrices; 6.3 Performance on Sparse Matrices; 6.4 Performance on Similar-Row Matrices; 6.5 Performance on SuiteSparse Matrix Collection; 6.6 Performance on Fullerene Adjacency Matrices; 7 Conclusion; References; Towards the Characterization of Max-Resolution Transformations of UCSs by UP-Resilience; 1 Introduction; 2 Definitions and Notations; 3 Preliminaries and Motivation; 4 Contributions; 5 Conclusion