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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    MATH 553 | Course Introduction and Application Information

    Course Name
    Optimization
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    MATH 553
    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
    Upon the completion of this course the student expected to gain an aiding tool for solving realworld problems which require LP approach, be able to have a detailed information about fundamental methods of LP such as Graphical method and the simplex method. Students will have detailed information about Duality and Sensitivity of L.P's. They will have information on unconstraint and nonlinear optimization models and methods to solve this problem.
    Learning Outcomes

    The students who succeeded in this course;

    • will be able to model real life problems using Linear programming(LP).
    • will be able to solve problems using methods of LP such as Graphical method, the simplex method and BigM method..
    • will be able to analyze dual problem and marginal costs using Duality theorem.
    • will be able to inspect optimization problems with Sensitivity analysis.
    • will be able to develop unconstraint and nonlinear optimization models.
    • will be able to solve unconstraint and nonlinear optimization problems.
    Course Description Linear Programming: Modeling, Solution Methods, Duality in linear programming; Nonlinear programming: First and second order optimality conditions for unconstrained problems, Lagrange multipliers, convexity in mathematical programming, The KuhnTucker theorem; Discrete optimization.

     



    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 Basic Linear Algebra Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    2 Introduction to Linear Programming Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    3 The Simplex Algorithm Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    4 Sensitivity Analysis Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    5 Duality Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    6 Integer Programming Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    7 Advanced Topics in LP Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    8 Nonlinear Programming Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    9 Nonlinear Programming Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    10 Probablistic Inventory Models Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    11 Game Theory Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    12 Exam Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    13 Project Presentations Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    14 Project Presentations Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    15 Semester Review Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
    16 Final Exam

     

    Course Notes/Textbooks

    "Operations Research Applications and Algorithms" by W.L.Winston, Duxbury Press 4th Edition , 1997, ISBN-13: 978-0534520205
    Suggested Readings/Materials

    "Introduction to Operations Research", 7th Edition, Hillier, Liberman, McGrawHill, 2001. ISBN-13: 978-0072461213

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    Presentation / Jury
    2
    20
    Project
    1
    20
    Seminar / Workshop
    Oral Exams
    Midterm
    2
    25
    Final Exam
    1
    35
    Total

    Weighting of Semester Activities on the Final Grade
    5
    65
    Weighting of End-of-Semester Activities on the Final Grade
    1
    35
    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
    10
    6
    60
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    0
    Presentation / Jury
    2
    4
    8
    Project
    1
    9
    9
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    2
    25
    50
    Final Exam
    1
    50
    50
        Total
    225

     

    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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