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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    IE 520 | Course Introduction and Application Information

    Course Name
    Constraint Programming
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    IE 520
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator -
    Course Lecturer(s) -
    Assistant(s) -
    Course Objectives The goal of this course is to have each student acquire the knowledge on a new programming paradigm based on constraints over finite domains and provide experience of how to use these methods for solving combinatorial problems. The Optimization Programming Language (OPL) and its Software Package will be introduced and used throughout the semester to model various combinatorial problems as constraint programs, as well as to write specific search methods.
    Learning Outcomes

    The students who succeeded in this course;

    • Describe how a generic constraint solver works, by giving its architecture and explaining the principles it is based on
    • Model a combinatorial problem as a socalled constraint program, using the primitive constraints of a given socalled constraint solver
    • Devise (empirically) a suitable heuristic control of the search that is to be performed by the constraint program
    • Formulate and compare (empirically) several alternative constraint programs for the same combinatorial problem
    • Evaluate (empirically) the computational consequences of having a controlled redundancy among the variables or among the constraints
    • Identify and break (some of the) symmetries in a constraint program for a combinatorial problem, thereby speeding up its execution
    • Enhance a given constraint solver with an additional constraint, by devising a filtering algorithm for it, and argue why it is faster than its reformulation based on the existing constraints of the solver
    • Describe briefly some other technologies for modelling and solving combinatorial problems, such as integer linear programming and local search
    Course Description The basic concepts of constraint programming. Modeling combinatorial problems in terms of constraints. Constraint consistency and propagation. Global constraints and their propagation algorithms. Search: construction of the search tree, exploration of the search tree, heuristics. Optimization. Advanced techniques: set variables, dealing with redundancy and symmetry. Implementation in a constraint programming language.

     



    Course Category

    		Core Courses
    Major Area Courses
    Supportive Courses
    Media and Management Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Constraint satisfaction problems Lecture notes
    2 Basic concepts of Constraint Programming Lecture notes
    3 Modelling combinatorial problem using OPL I Lecture notes
    4 Modelling combinatorial problem using OPL II Lecture notes
    5 Constraint consistency and propagation I Lecture notes
    6 Constraint consistency and propagation II Lecture notes
    7 Global constraints Lecture notes
    8 Ara sınav / Midterm Lecture notes
    9 Search heuristics I Lecture notes
    10 Search heuristics II Lecture notes
    11 Set variables Lecture notes
    12 Symmetry Lecture notes
    13 Optimisation problems Lecture notes
    14 Discussions, Research and Presentations I Lecture notes
    15 Discussions, Research and Presentations II Lecture notes
    16 Review of the Semester

     

    Course Notes/Textbooks Course slides
    Suggested Readings/Materials Related research papers

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    Presentation / Jury
    1
    30
    Project
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    30
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    60
    Weighting of End-of-Semester Activities on the Final Grade
    40
    Total

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Theoretical Course Hours
    (Including exam week: 16 x total hours)
    16
    3
    48
    Laboratory / Application Hours
    (Including exam week: '.16.' x total hours)
    16
    0
    Study Hours Out of Class
    15
    6
    90
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    0
    Presentation / Jury
    1
    5
    5
    Project
    0
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    15
    15
    Final Exam
    1
    22
    22
        Total
    180

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    		 PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.
    -
    -
    -
    X
    -
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    -
    X
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.
    -
    X
    -
    -
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    X
    -
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    X
    -
    -
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.
    -
    -
    X
    -
    -
    7 Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.
    X
    -
    -
    -
    -
    8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.
    -
    -
    -
    X
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    X
    -
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    X
    -
    -
    11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
    -
    X
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    X
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     

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