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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    IE 534 | Course Introduction and Application Information

    Course Name
    Nonlinear Programming
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    IE 534
    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 aim of this course is to develop knowledge of different theoretical aspects of nonlinear programming and convex optimization and to give graduate and PhD students the theoretical background on convex analysis and on the theory of optimality conditions, and to provide them with a foundation sufficient to use basic optimization in their own research work and/or to pursue more specialized studies involving optimization theory.
    Learning Outcomes

    The students who succeeded in this course;

    • Will be able to interpret convex sets and convex functions
    • Will be able to analyze extreme points and extreme directions of convex sets
    • Will be able to analyze some topological properties of convex sets and convex functions
    • Will be able to use the concept of convexity in the analysis of nonlinear programming problems
    • Will be able to interpret optimality conditions for nonlinear programming problems
    Course Description The course emphasizes the unifying themes such that convex sets and convex functions, their topological properties, separation theorems and optimality conditions for convex optimization problems.

     



    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 Convex Analysis review and basics
    2 Mathematical Preliminaries
    3 Mathematical Preliminaries
    4 Nonlinear Optimization: Line searches
    5 Nonlinear Optimization: Line searches
    6 Unconstrained Problems
    7 Unconstrained Problems
    8 Midterm
    9 Constrained Problems
    10 Constrained Problems
    11 Linearly Constrained Problems
    12 Lagrangian Duality
    13 Paper Presentations
    14 Review and Project Presentations
    15 -
    16 Final

     

    Course Notes/Textbooks Nonlinear Programming. Theory and Algorithms., Mokhtar S. Bazaraa, Hanif D. Sherali, C.M. Shetty, John Wiley & Sons, ISBN 0471557935.
    Suggested Readings/Materials Bertsekas, D. Nonlinear Programming, Second Edition, Athena Scientific Publishing, 1999.

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    1
    10
    Portfolio
    Homework / Assignments
    1
    10
    Presentation / Jury
    Project
    1
    25
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    25
    Final Exam
    1
    30
    Total

    Weighting of Semester Activities on the Final Grade
    4
    70
    Weighting of End-of-Semester Activities on the Final Grade
    1
    30
    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
    14
    8
    112
    Field Work
    0
    Quizzes / Studio Critiques
    1
    0
    Portfolio
    0
    Homework / Assignments
    4
    6
    24
    Presentation / Jury
    6
    0
    Project
    0
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    10
    10
    Final Exam
    1
    15
    15
        Total
    209

     

    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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