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Principles and Practice of Constraint Programming

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Cover of 'Principles and Practice of Constraint Programming'

Table of Contents

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    Book Overview
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    Chapter 1 Encoding Linear Constraints with Implication Chains to CNF
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    Chapter 2 Anytime Hybrid Best-First Search with Tree Decomposition for Weighted CSP
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    Chapter 3 Improved Constraint Propagation via Lagrangian Decomposition
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    Chapter 4 Strengthening Convex Relaxations with Bound Tightening for Power Network Optimization
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    Chapter 5 Broken Triangles Revisited
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    Chapter 6 A Microstructure-Based Family of Tractable Classes for CSPs
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    Chapter 7 The Unary Resource with Transition Times
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    Chapter 8 A Global Constraint for a Tractable Class of Temporal Optimization Problems
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    Chapter 9 Exploiting GPUs in Solving (Distributed) Constraint Optimization Problems with Dynamic Programming
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    Chapter 10 Conflict Ordering Search for Scheduling Problems
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    Chapter 11 Simple and Scalable Time-Table Filtering for the Cumulative Constraint
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    Chapter 12 General Bounding Mechanism for Constraint Programs
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    Chapter 13 Principles and Practice of Constraint Programming
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    Chapter 14 Upper and Lower Bounds on the Time Complexity of Infinite-Domain CSPs
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    Chapter 15 Generalized Totalizer Encoding for Pseudo-Boolean Constraints
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    Chapter 16 Smaller Selection Networks for Cardinality Constraints Encoding
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    Chapter 17 PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining
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    Chapter 18 On Tree-Preserving Constraints
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    Chapter 19 Modeling and Solving Project Scheduling with Calendars
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    Chapter 20 Deterministic Estimation of the Expected Makespan of a POS Under Duration Uncertainty
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    Chapter 21 A Parallel, Backjumping Subgraph Isomorphism Algorithm Using Supplemental Graphs
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    Chapter 22 Automated Auxiliary Variable Elimination Through On-the-Fly Propagator Generation
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    Chapter 23 Principles and Practice of Constraint Programming
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    Chapter 24 Exact Sampling for Regular and Markov Constraints with Belief Propagation
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    Chapter 25 Randomness as a Constraint
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    Chapter 26 Quasipolynomial Simulation of DNNF by a Non-determinstic Read-Once Branching Program
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    Chapter 27 MiniSearch: A Solver-Independent Meta-Search Language for MiniZinc
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    Chapter 28 Two Clause Learning Approaches for Disjunctive Scheduling
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    Chapter 29 Bounding an Optimal Search Path with a Game of Cop and Robber on Graphs
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    Chapter 30 Restricted Path Consistency Revisited
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    Chapter 31 Machine Learning of Bayesian Networks Using Constraint Programming
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    Chapter 32 Hybridization of Interval CP and Evolutionary Algorithms for Optimizing Difficult Problems
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    Chapter 33 A General Framework for Reordering Agents Asynchronously in Distributed CSP
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    Chapter 34 Automatically Generating Streamlined Constraint Models with Essence and Conjure
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    Chapter 35 Constraint-Based Local Search for Finding Node-Disjoint Bounded-Paths in Optical Access Networks
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    Chapter 36 Open Packing for Facade-Layout Synthesis Under a General Purpose Solver
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    Chapter 37 Power Capping in High Performance Computing Systems
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    Chapter 38 A Constraint-Based Approach to the Differential Harvest Problem
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    Chapter 39 Constrained Minimum Sum of Squares Clustering by Constraint Programming
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    Chapter 40 A Constraint Programming Approach for Non-preemptive Evacuation Scheduling
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    Chapter 41 Solving Segment Routing Problems with Hybrid Constraint Programming Techniques
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    Chapter 42 Modeling Universal Instruction Selection
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    Chapter 43 Optimizing the Cloud Service Experience Using Constraint Programming
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    Chapter 44 Find Your Way Back: Mobility Profile Mining with Constraints
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    Chapter 45 Joint Vehicle and Crew Routing and Scheduling
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    Chapter 46 Constructing Sailing Match Race Schedules: Round-Robin Pairing Lists
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    Chapter 47 Design and Evaluation of a Constraint-Based Energy Saving and Scheduling Recommender System
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    Chapter 48 Scheduling Running Modes of Satellite Instruments Using Constraint-Based Local Search
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    Chapter 49 Erratum to: Modeling Universal Instruction Selection
Attention for Chapter 4: Strengthening Convex Relaxations with Bound Tightening for Power Network Optimization
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Chapter title
Strengthening Convex Relaxations with Bound Tightening for Power Network Optimization
Chapter number 4
Book title
Principles and Practice of Constraint Programming
Published by
Springer, Cham, August 2015
DOI 10.1007/978-3-319-23219-5_4
Book ISBNs
978-3-31-923218-8, 978-3-31-923219-5
Authors

Carleton Coffrin, Hassan L. Hijazi, Pascal Van Hentenryck

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 3%
Denmark 1 3%
Belgium 1 3%
Unknown 26 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 31%
Researcher 6 21%
Student > Doctoral Student 3 10%
Professor 2 7%
Student > Master 2 7%
Other 2 7%
Unknown 5 17%
Readers by discipline Count As %
Engineering 11 38%
Computer Science 5 17%
Energy 4 14%
Mathematics 2 7%
Business, Management and Accounting 1 3%
Other 0 0%
Unknown 6 21%