Constraint Programming
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Algorithmic Design and Techniques at Edx: Syllabus
Algorithmic Design and Techniques at edX: Syllabus February 18, 2018 Contents 1 Welcome!2 2 Who This Class Is For2 3 Meet Your Instructors2 4 Prerequisites3 5 Course Overview4 6 Learning Objectives5 7 Estimated Workload5 8 Grading 5 9 Deadlines6 10 Verified Certificate6 11 Forum 7 1 1 Welcome! Thank you for joining Algorithmic Design and Techniques at edX! In this course, you will learn not only the basic algorithmic techniques, but also how to write efficient, clean, and reliable code. 2 Who This Class Is For Programmers with basic experience looking to understand the practical and conceptual underpinnings of algorithms, with the goal of becoming more effective software engi- neers. Computer science students and researchers as well as interdisciplinary students (studying electrical engineering, mathematics, bioinformatics, etc.) aiming to get more profound understanding of algorithms and hands-on experience implementing them and applying for real-world problems. Applicants who want to prepare for an interview in a high-tech company. 3 Meet Your Instructors Daniel Kane is an associate professor at the University of California, San Diego with a joint appointment between the Department of Com- puter Science and Engineering and the Department of Mathematics. He has diverse interests in mathematics and theoretical computer science, though most of his work fits into the broad categories of number theory, complexity theory, or combinatorics. Alexander S. Kulikov is a senior research fellow at Steklov Mathemat- ical Institute of the Russian Academy of Sciences, Saint Petersburg, Russia and a lecturer at the Department of Computer Science and Engineering at University of California, San Diego, USA. -
The Design and Implementation of Object-Constraint Programming
Felgentreff, The Design and Implementation of Object-Constraint Programming The Design and Implementation of Object-Constraint Programming von Tim Felgentreff Dissertation zur Erlangung des akademischen Grades des Doktor der Naturwissenschaften (Doctor rerum naturalium) vorgelegt der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam. Betreuer Prof. Dr. Robert Hirschfeld Fachgebiet Software-Architekturen Hasso-Plattner-Institut Universität Potsdam 21. April 2017 Erklärung Hiermit erkläre ich an Eides statt, dass ich die vorliegende Dissertation selbst angefertigt und nur die im Literaturverzeichnis aufgeführten Quellen und Hilfsmittel verwendet habe. Diese Dissertation oder Teile davon wurden nicht als Prüfungsarbeit für eine staatliche oder andere wissenschaftliche Prüfung eingereicht. Ich versichere weiterhin, dass ich diese Arbeit oder eine andere Abhandlung nicht bei einer anderen Fakultät oder einer anderen Universität eingereicht habe. Potsdam, den 21. April 2017 Tim Felgentreff v Abstract Constraints allow developers to specify properties of systems and have those properties be main- tained automatically. This results in compact declarations to describe interactive applications avoid- ing scattered code to check and imperatively re-satisfy invariants in response to user input that perturbs the system. Constraints thus provide flexibility and expressiveness for solving complex problems and maintaining a desired system state. Despite these advantages, constraint program- ming is not yet widespread, with imperative programming still being the norm. There is a long history of research on constraint programming as well as its integration with general purpose programming languages, especially from the imperative paradigm. However, this integration typically does not unify the constructs for encapsulation and abstraction from both paradigms and often leads to a parallel world of constraint code fragments and abstractions inter- mingled with the general purpose code. -
Constraint Satisfaction with Preferences Ph.D
MASARYK UNIVERSITY BRNO FACULTY OF INFORMATICS } Û¡¢£¤¥¦§¨ª«¬Æ !"#°±²³´µ·¸¹º»¼½¾¿45<ÝA| Constraint Satisfaction with Preferences Ph.D. Thesis Brno, January 2001 Hana Rudová ii Acknowledgements I would like to thank my supervisor, Ludˇek Matyska, for his continuous support, guidance, and encouragement. I am very grateful for his help and advice, which have allowed me to develop both as a person and as the avid student I am today. I want to thank to my husband and my family for their support, patience, and love during my PhD study and especially during writing of this thesis. This research was supported by the Universities Development Fund of the Czech Repub- lic under contracts # 0748/1998 and # 0407/1999. Declaration I declare that this thesis was composed by myself, and all presented results are my own, unless otherwise stated. Hana Rudová iii iv Contents 1 Introduction 1 1.1 Thesis Outline ..................................... 1 2 Constraint Satisfaction 3 2.1 Constraint Satisfaction Problem ........................... 3 2.2 Optimization Problem ................................ 4 2.3 Solution Methods ................................... 5 2.4 Constraint Programming ............................... 6 2.4.1 Global Constraints .............................. 7 3 Frameworks 11 3.1 Weighted Constraint Satisfaction .......................... 11 3.2 Probabilistic Constraint Satisfaction ........................ 12 3.2.1 Problem Definition .............................. 13 3.2.2 Problems’ Lattice ............................... 13 3.2.3 Solution -
Constraint Programming and Operations Research
Noname manuscript No. (will be inserted by the editor) Constraint Programming and Operations Research J. N. Hooker and W.-J. van Hoeve November 2017 Abstract We present an overview of the integration of constraint program- ming (CP) and operations research (OR) to solve combinatorial optimization problems. We interpret CP and OR as relying on a common primal-dual solution approach that provides the basis for integration using four main strategies. The first strategy tightly interweaves propagation from CP and relaxation from OR in a single solver. The second applies OR techniques to domain filtering in CP. The third decomposes the problem into a portion solved by CP and a portion solved by OR, using CP-based column generation or logic-based Benders decomposition. The fourth uses relaxed decision diagrams developed for CP propagation to help solve dynamic programming models in OR. The paper cites a significant fraction of the literature on CP/OR integration and concludes with future perspectives. Keywords Constraint Programming, Operations Research, Hybrid Optimization 1 Introduction Constraint programming (CP) and operations research (OR) have the same overall goal. They strive to capture a real-world situation in a mathematical model and solve it efficiently. Both fields use constraints to build the model, often in conjunction with an objective function to evaluate solutions. It is therefore only natural that the two fields join forces to solve problems. J. N. Hooker Carnegie Mellon University, Pittsburgh, USA E-mail: [email protected] W.-J. van Hoeve Carnegie Mellon University, Pittsburgh, USA E-mail: [email protected] 2 J. N. -
Chapter 1 GLOBAL CONSTRAINTS and FILTERING ALGORITHMS
Chapter 1 GLOBAL CONSTRAINTS AND FILTERING ALGORITHMS Jean-Charles R´egin ILOG 1681, route des Dolines, Sophia Antipolis, 06560 Valbonne, France [email protected] Abstract Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently used global constraints are presented. In addition, the filtering algorithms establishing arc consistency for two useful con- straints, the alldiff and the global cardinality constraints, are fully de- tailed. Filtering algorithms are also considered from a theoretical point of view: three different ways to design filtering algorithms are described and the quality of the filtering algorithms studied so far is discussed. A categorization is then proposed. Over-constrained problems are also mentioned and global soft constraints are introduced. Keywords: Global constraint, filtering algorithm, arc consistency, alldiff, global car- dinality constraint, over-constrained problems, global soft constraint, graph theory, matching. 1. Introduction A constraint network (CN) consists of a set of variables; domains of possible values associated with each of these variables; and a set of con- straints that link up the variables and define the set of combinations of values that are allowed. The search for an instantiation of all vari- ables that satisfies all the constraints is called a Constraint Satisfaction Problem (CSP), and such an instantiation is called a solution of a CSP. A lot of problems can be easily coded in terms of CSP. For instance, CSP has already been used to solve problems of scene analysis, place- 1 2 ment, resource allocation, crew scheduling, time tabling, scheduling, fre- quency allocation, car sequencing, and so on. -
From Local to Global Consistency in Temporal Constraint Networks
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Theoretical Computer Science ELSEVIER Theoretical Computer Science 173 (1997) 89-112 From local to global consistency in temporal constraint networks Manolis Koubarakis * Dept. of Computation, (/MIST, P.O. Box 88, Manchester A460 lQD, UK Abstract We study the problem of global consistency for several classes of quantitative temporal con- straints which include inequalities, inequations and disjunctions of inequations. In all cases that we consider we identify the level of local consistency that is necessary and sufficient for achiev- ing global consistency and present an algorithm which achieves this level. As a byproduct of our analysis, we also develop an interesting minimal network algorithm. 1. Introduction One of the most important notions found in the constraint satisfaction literature is global consistency [S]. In a globally consistent constraint set all interesting constraints are explicitly represented and the projection of the solution set on any subset of the variables can be computed by simply collecting the constraints involving these vari- ables. An important consequence of this property is that a solution can be found by backtrack-free search [6]. Enforcing global consistency can take an exponential amount of time in the worst case [S, 11. As a result it is very important to identify cases in which local consistency, which presumably can be enforced in polynomial time, implies global consistency [2]. In this paper we study the problem of enforcing global consistency for sets of quanti- tative temporal constraints over the rational (or real) numbers. -
Backtracking Search (Csps) ■Chapter 5 5.3 Is About Local Search Which Is a Very Useful Idea but We Won’T Cover It in Class
CSC384: Intro to Artificial Intelligence Backtracking Search (CSPs) ■Chapter 5 5.3 is about local search which is a very useful idea but we won’t cover it in class. 1 Hojjat Ghaderi, University of Toronto Constraint Satisfaction Problems ● The search algorithms we discussed so far had no knowledge of the states representation (black box). ■ For each problem we had to design a new state representation (and embed in it the sub-routines we pass to the search algorithms). ● Instead we can have a general state representation that works well for many different problems. ● We can build then specialized search algorithms that operate efficiently on this general state representation. ● We call the class of problems that can be represented with this specialized representation CSPs---Constraint Satisfaction Problems. ● Techniques for solving CSPs find more practical applications in industry than most other areas of AI. 2 Hojjat Ghaderi, University of Toronto Constraint Satisfaction Problems ●The idea: represent states as a vector of feature values. We have ■ k-features (or variables) ■ Each feature takes a value. Domain of possible values for the variables: height = {short, average, tall}, weight = {light, average, heavy}. ●In CSPs, the problem is to search for a set of values for the features (variables) so that the values satisfy some conditions (constraints). ■ i.e., a goal state specified as conditions on the vector of feature values. 3 Hojjat Ghaderi, University of Toronto Constraint Satisfaction Problems ●Sudoku: ■ 81 variables, each representing the value of a cell. ■ Values: a fixed value for those cells that are already filled in, the values {1-9} for those cells that are empty. -
Prolog Lecture 6
Prolog lecture 6 ● Solving Sudoku puzzles ● Constraint Logic Programming ● Natural Language Processing Playing Sudoku 2 Make the problem easier 3 We can model this problem in Prolog using list permutations Each row must be a permutation of [1,2,3,4] Each column must be a permutation of [1,2,3,4] Each 2x2 box must be a permutation of [1,2,3,4] 4 Represent the board as a list of lists [[A,B,C,D], [E,F,G,H], [I,J,K,L], [M,N,O,P]] 5 The sudoku predicate is built from simultaneous perm constraints sudoku( [[X11,X12,X13,X14],[X21,X22,X23,X24], [X31,X32,X33,X34],[X41,X42,X43,X44]]) :- %rows perm([X11,X12,X13,X14],[1,2,3,4]), perm([X21,X22,X23,X24],[1,2,3,4]), perm([X31,X32,X33,X34],[1,2,3,4]), perm([X41,X42,X43,X44],[1,2,3,4]), %cols perm([X11,X21,X31,X41],[1,2,3,4]), perm([X12,X22,X32,X42],[1,2,3,4]), perm([X13,X23,X33,X43],[1,2,3,4]), perm([X14,X24,X34,X44],[1,2,3,4]), %boxes perm([X11,X12,X21,X22],[1,2,3,4]), perm([X13,X14,X23,X24],[1,2,3,4]), perm([X31,X32,X41,X42],[1,2,3,4]), perm([X33,X34,X43,X44],[1,2,3,4]). 6 Scale up in the obvious way to 3x3 7 Brute-force is impractically slow There are very many valid grids: 6670903752021072936960 ≈ 6.671 × 1021 Our current approach does not encode the interrelationships between the constraints For more information on Sudoku enumeration: http://www.afjarvis.staff.shef.ac.uk/sudoku/ 8 Prolog programs can be viewed as constraint satisfaction problems Prolog is limited to the single equality constraint: – two terms must unify We can generalise this to include other types of constraint Doing so leads -
Quantum-Accelerated Constraint Programming
Quantum-accelerated constraint programming Kyle E. C. Booth1,2, Bryan O’Gorman1,3, Jeffrey Marshall1,2, Stuart Hadfield1,2, and Eleanor Rieffel1 1Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center, Moffett Field, CA 94035, USA 2USRA Research Institute for Advanced Computer Science (RIACS), Mountain View, CA 94043, USA 3Berkeley Quantum Information and Computation Center, University of California, Berkeley, CA 94720, USA Constraint programming (CP) is a paradigm used to model and solve con- straint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of in- ference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the alldifferent global constraint and discuss its applicability to a broader family of global constraints with sim- ilar structure. We propose frameworks for the integration of quantum filtering algorithms within both classical and quantum backtracking search schemes, including a novel hybrid classical-quantum backtracking search method. This work suggests that CP is a promising candidate application for early fault- tolerant quantum computers and beyond. 1 Introduction Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems [1]. In CP, a problem is expressed in terms of a declarative model, identifying variables and constraints, and the model is evaluated using a general-purpose constraint solver, leveraging techniques from a variety of fields including artificial intelligence, computer science, and operations research. CP has successfully been used to approach challenging problems in areas such as scheduling, planning, and vehicle routing [1–3]. -
The Logic of Satisfaction Constraint
Artificial Intelligence 58 (1992) 3-20 3 Elsevier ARTINT 948 The logic of constraint satisfaction Alan K. Mackworth* Department of Computer Science, University of British Columbia, Vancouver, BC, Canada V6T 1 W5 Abstract Mackworth, A.K., The logic of constraint satisfaction, Artificial Intelligence 58 (1992) 3-20. The constraint satisfaction problem (CSP) formalization has been a productive tool within Artificial Intelligence and related areas. The finite CSP (FCSP) framework is presented here as a restricted logical calculus within a space of logical representation and reasoning systems. FCSP is formulated in a variety of logical settings: theorem proving in first order predicate calculus, propositional theorem proving (and hence SAT), the Prolog and Datalog approaches, constraint network algorithms, a logical interpreter for networks of constraints, the constraint logic programming (CLP) paradigm and propositional model finding (and hence SAT, again). Several standard, and some not-so-standard, logical methods can therefore be used to solve these problems. By doing this we obtain a specification of the semantics of the common approaches. This synthetic treatment also allows algorithms and results from these disparate areas to be imported, and specialized, to FCSP; the special properties of FCSP are exploited to achieve, for example, completeness and to improve efficiency. It also allows export to the related areas. By casting CSP both as a generalization of FCSP and as a specialization of CLP it is observed that some, but not all, FCSP techniques lift to CSP and thereby to CLP. Various new connections are uncovered, in particular between the proof-finding approaches and the alternative model-finding ap- proaches that have arisen in depiction and diagnosis applications. -
Constraint Satisfaction Problems (Backtracking Search)
CSC384: Introduction to Artificial Intelligence Constraint Satisfaction Problems (Backtracking Search) • Chapter 6 – 6.1: Formalism – 6.2: Constraint Propagation – 6.3: Backtracking Search for CSP – 6.4 is about local search which is a very useful idea but we won’t cover it in class. Torsten Hahmann, CSC384 Introduction to Artificial Intelligence, University of Toronto, Fall 2011 1 Acknowledgements • Much of the material in the lecture slides comes from Fahiem Bacchus, Sheila McIlraith, and Craig Boutilier. • Some slides come from a tutorial by Andrew Moore via Sonya Allin. • Some slides are modified or unmodified slides provided by Russell and Norvig. Torsten Hahmann, CSC384 Introduction to Artificial Intelligence, University of Toronto, Fall 2011 2 Constraint Satisfaction Problems (CSP) • The search algorithms we discussed so far had no knowledge of the states representation (black box). – For each problem we had to design a new state representation (and embed in it the sub-routines we pass to the search algorithms). • Instead we can have a general state representation that works well for many different problems. • We can then build specialized search algorithms that operate efficiently on this general state representation. • We call the class of problems that can be represented with this specialized representation: CSPs – Constraint Satisfaction Problems. Torsten Hahmann, CSC384 Introduction to Artificial Intelligence, University of Toronto, Fall 2011 3 Constraint Satisfaction Problems (CSP) •The idea: represent states as a vector of feature values. – k-features (or variables) – Each feature takes a value. Each variable has a domain of possible values: • height = {short, average, tall}, • weight = {light, average, heavy} •In CSPs, the problem is to search for a set of values for the features (variables) so that the values satisfy some conditions (constraints). -
Constraint Programming
Constraint programming Alexander Bockmayr John N. Hooker May 2003 Contents 1 Introduction 2 1.1 ConstraintsasProcedures . ... 2 1.2 ParallelswithBranchandCut . .. 3 1.3 ConstraintSatisfaction. .... 4 1.4 HybridMethods ................................. 6 1.5 PerformanceIssues ............................... 7 2 Constraints 8 2.1 WhatisaConstraint? .............................. 8 2.2 Arithmeticversus SymbolicConstraints . ....... 9 2.3 GlobalConstraints ............................... 9 2.4 LocalConsistency ................................ 10 2.5 ConstraintPropagation . .. 11 2.6 Filtering Algorithms for Global Constraints . ......... 12 2.7 Modeling in Constraint Programming : An Illustrating Example ...... 14 3 Search 17 3.1 VariableandValueOrdering . .. 17 3.2 CompleteSearch ................................. 18 3.3 HeuristicSearch ................................. 19 4 Hybrid Methods 19 4.1 Branch,InferandRelax . .. ... ... ... ... ... .. ... ... .. 20 4.2 BendersDecomposition . .. ... ... ... ... ... .. ... ... .. 22 4.3 MachineSchedulingExample . .. 23 4.4 Continuous Relaxations for Global Constraints . ......... 25 4.5 OtherApplications............................... 26 5 Constraint Programming Languages and Systems 26 5.1 High-levelModelingLanguages . .... 27 5.2 ConstraintProgrammingSystems. .... 27 1 Introduction A discrete optimization problem can be given a declarative or procedural formulation, and both have their advantages. A declarative formulation simply states the constraints and objective function. It allows one to describe