Del 12333 - Lecture Notes in Computer Science

Principles and Practice of Constraint Programming

26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings

Häftad, Engelska, 2020

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This book constitutes the proceedings of the 26th International Conference on Principles and Practice of Constraint Programming, CP 2020, held in Louvain-la-Neuve, Belgium, in September 2020.

Produktinformation

Utgivningsdatum2020-09-07
Mått155 x 235 x 53 mm
Vikt1 463 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor972
FörlagSpringer Nature Switzerland AG
ISBN9783030584740

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Technical Track.- Dashed strings and the replace(-all) constraint.- Combinatorial Search in CP-Based Iterated Belief Propagation.- Replication-Guided Enumeration of Minimal Unsatisfiable Subsets.- Solving Satisfaction Problems using Large-Neighbourhood Search.- Quantum-accelerated global constraint ltering.- Pure MaxSAT and Its Applications to Combinatorial Optimization via Linear Local Search.- Tractable Fragments of Temporal Sequences of Topological Information.- Strengthening neighbourhood substitution.- Effective Encodings of Constraint Programming Models to SMT.- Watched Propagation of 0-1 Integer Linear Constraints.- Bounding Linear Programs by Constraint Propagation: Application to Max-SAT.- On Relation Between Constraint Propagation and Block-Coordinate Descent in Linear Programs.- DPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees.- Aggregation and Garbage Collection for Online Optimization.- Treewidth-Aware Quantifier Elimination and Expansion for QCSP.- A Time Leap Challenge for SAT-Solving.- Breaking Symmetries with RootClique and LexTopsort.- Towards Faster Reasoners by using Transparent Huge Pages.- The argmax constraint.- A branch-and-bound algorithm to rigorously enclose the round-off errors.- Certifying Solvers for Clique and Maximum Common (Connected) Subgraph Problems.- Solving the Group Cumulative Scheduling Problem with CPO and ACO.- Phase Transition Behaviour in Knowledge Compilation.- A Faster Exact Algorithm to Count X3SAT Solutions.- Visualizations to Summarize Search Behavior.- Parallelization of TSP solving in CP.- Using Resolution Proofs to Analyse CDCL Solvers.- Large Neighborhood Search for Temperature Control with Demand Response.- Core-Guided Model Reformulation.- Filtering rules for ow time minimization in a Parallel Machine Scheduling Problem.- MaxSAT-Based Postprocessing for Treedepth.- Effective Perturbations for Constraint Solving.- Finding the Hardest Formulas for Resolution.- HADDOCK: A Language and Architecture for Decision Diagram Compilation.- Towards a Generic Interval Solver for Differential-Algebraic CSP.- abstractXOR: A global constraint dedicated to differential cryptanalysis.- In Pursuit of an Efficient SAT Encoding for the Hamiltonian Cycle Problem.- Application Track.- Leveraging Reinforcement Learning, Constraint Programming and Local Search: A Case Study in Car Manufacturing.- Computing the Local Aromaticity of Benzenoids Thanks to Constraint Programming.- Using Constraint Programming to Generate Benzenoid Structures in Theoretical Chemistry.- RobTest: A CP Approach to Generate Maximal Test Trajectories for Industrial Robots.- A Two-Phase Constraint Programming Model for Examination Timetabling at University College Cork.- Exact Approaches to the Multi-Agent Collective Construction Problem.- The Confidence Constraint: A Step Towards Stochastic CP Solvers.- Parity (XOR) Reasoning for the Index Calculus Attack.- Constraint-Based Software Diversification forEfficient Mitigation of Code-Reuse Attacks.- CP and Data Science and Machine Learning.- Pushing data into CP models using Graphical Model Learning and Solving.- Generating Random Logic Programs Using Constraint Programming.- Towards Formal Fairness in Machine Learning.- Verifying Equivalence Properties of Neural Networks with ReLU Activation Functions.- Learning the Satisfiability of Pseudo-Boolean Problem with Graph Neural Networks.- A Machine Learning based Splitting Heuristic for Divide-and-Conquer Solvers.- Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models.- Omissions in Constraint Acquisition.- Computing Optimal Decision Sets with SAT.