Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.

The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.

The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.

The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.

- Covers the whole field of constraint programming
- Survey-style chapters
- Five chapters on applications

Table of Contents

Foreword (Ugo Montanari) 
Part I : Foundations
Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) 
Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) 
Chapter 3. Constraint Propagation (Christian Bessiere) 
Chapter 4. Backtracking Search Algorithms (Peter van Beek) 
Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) 
Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel)
Chapter 7. Tractable Structures for CSPs (Rina Dechter)
Chapter 8. The Complexity of Constraint Languages
(David Cohen, Peter Jeavons) 
Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) 
Chapter 10. Symmetry in Constraint Programming
(Ian P. Gent, Karen E. Petrie, Jean-Francois Puget)
Chapter 11. Modelling (Barbara M. Smith) 
Part II : Extensions, Languages, and Applications

Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) 
Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte)
Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) 
Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) 
Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) 
Chapter 17. Constraints over Structured Domains
(Carmen Gervet) 
Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh)
Chapter 19. Temporal CSPs (Manolis Koubarakis)
Chapter 20. Distributed Constraint Programming
(Boi Faltings) 
Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel)
Chapter 22. Constraint-Based Scheduling and Planning
(Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten)
Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) 
Chapter 24. Configuration (Ulrich Junker)
Chapter 25. Constraint Applications in Networks
(Helmut Simonis)
Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)

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Handbook of Constraint Programming

We survey parallel programming models and languages using six criteria to assess their suitability for realistic portable parallel programming. We argue that an ideal model should by easy to program, should have a software development methodology, should be architecture-independent, should be easy to understand, should guarantee performance, and should provide accurate information about the cost of programs. These criteria reflect our belief that developments in parallelism must be driven by a parallel software industry based on portability and efficiency. We consider programming models in six categories, depending on the level of abstraction they provide. Those that are very abstract conceal even the presence of parallelism at the software level. Such models make software easy to build and port, but efficient and predictable performance is usually hard to achieve. At the other end of the spectrum, low-level models make all of the messy issues of parallel programming explicit (how many threads, how to place them, how to express communication, and how to schedule communication), so that software is hard to build and not very portable, but is usually efficient. Most recent models are near the center of this spectrum, exploring the best tradeoffs between expressiveness and performance. A few models have achieved both abstractness and efficiency. Both kinds of models raise the possibility of parallelism as part of the mainstream of computing.

/pdf/models-and-languages-for-parallel-computation-1sticvkm4u.pdf

Models and languages for parallel computation

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 138 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in machine learning testing.

/pdf/machine-learning-testing-survey-landscapes-and-horizons-3k25zixq6f.pdf

Machine Learning Testing: Survey, Landscapes and Horizons

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.

Materialized views are a natural embodiment of the ideas of precomputation and caching in databases. Instead of computing a query from scratch, a system can use results that have already been computed, stored, and kept in sync with database updates. The ability of materialized views to speed up queries benefits most database applications, ranging from traditional querying and reporting to web database caching, online analytical processing, and data mining. By reducing dependency on the availability of base data, materialized views have also laid much of the foundation for information integration and data warehousing systems. The database tradition of declarative querying distinguishes materialized views from generic applications of precomputation and caching in other contexts, and makes materialized views especially interesting, powerful, and challenging at the same time. Study of materialized views has generated a rich research literature and mature commercial implementations, aimed at providing efficient, effective, automated, and general solutions to the selection, use, and maintenance of materialized views. This monograph provides an accessible introduction and reference to materialized views, explains its core ideas, highlights its recent developments, and points out its sometimes subtle connections to other research topics in databases.

Materialized Views

An extension of the λ -calculus called BS λ is introduced as a formal basis for functional languages expressing bulk synchronous parallel algorithms. A confluence result is shown. The application of the calculus is illustrated by examples of program proofs and the associated notion of parallel reduction. The reduction process is interpreted in the BSP cost model.

A calculus of functional BSP programs

Although many skew-handling algorithms have been proposed for simple join operations, they remain generally inefficient in the case of θ-join and in the case of multi-join. A new method for self-balancing equi-join operations on shared-nothing (SN) machines is proposed here. It offers deterministic and near-perfect load balancing through flexible control of communications in intra-transaction parallelism. The new algorithm mixes a balanced data-distribution strategy with pure hash-join. Its predictably low join-product- and attribute-value skews make it suitable for repeated use in multi-join operations. Its tradeoff between balancing overhead and speedup is analyzed in the BSP (Bulk-synchronous parallel) computing model. The scalable model predicts a negligible join product skew and a near-linear speed-up in any combination of selectivity, skew and number of processors. This prediction is confirmed by a series of tests.

Frequency-adaptive join for shared nothing machines

Deep Learning (DL) techniques are now widespread and being integrated into many important systems. Their classification and recognition abilities ensure their relevance for multiple application domains far beyond pure signal processing. As a machine-learning technique that relies on training instead of explicit algorithm programming they offer a high degree of productivity. But recent research has shown that they can be vulnerable to attacks and the verification of their correctness is only just emerging as a scientific and engineering possibility. Moreover DL tools are not integrated into classical software engineering so software tools to specify, modify and verify them would make them even more mainstream as software-hardware systems. This paper surveys recent work and proposes research directions and methodologies for this purpose.

Towards formal methods and software engineering for deep learning: Security, safety and productivity for dl systems development

Monitoring and predicting resource consumption is a fundamental need when running a virtualized system. Predicting resources is necessary because cloud infrastructures use virtual resources on demand. Current monitoring tools are insufficient to predict resource usage of virtualized systems so, without proper monitoring, virtualized systems can suffer down time, which can directly affect cloud infrastructure. We propose a new modelling approach to the problem of resource prediction. Models are based on historical data to forecast short-term resource usages. We present here in detail three of our prediction models to forecast and monitor resources. We also show experimental results by using real-life data and an overall evaluation of this approach.

Gaétan Hains

Papers

A calculus of functional BSP programs

Frequency-adaptive join for shared nothing machines

Towards formal methods and software engineering for deep learning: Security, safety and productivity for dl systems development

A resource prediction model for virtualization servers

A conservative scheme for parallel interval narrowing