İzmir Ekonomi Üniversitesi
  • TÜRKÇE

  • GRADUATE SCHOOL

    M.SC. in Computer Engineering (With Thesis)

    MATH 602 | Course Introduction and Application Information

    Course Name
    Advanced Linear Algebra and Optimization
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    MATH 602
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Third Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator -
    Course Lecturer(s)
    Assistant(s)
    Course Objectives In this graduate course we introduce advanced mathematical optimization problem forms, models, and applications by introducing the relevant linear algebra concepts.
    Learning Outcomes

    The students who succeeded in this course;

    • will be able to modeloptimization problems.
    • will be able to develop and apply optimization related theorems.
    • will be able to solve decision problems using Simplex Algorithm.
    • will be able to calculate local optimum solution of a given problem.
    • will be able to calculate global optimum solution of a given problem.
    • will be able to analyze advanced linear systems.
    Course Description This course provides essential materials for analyzing advanced mathematical optimization problem forms, models, and applications by introducing the relevant linear algebra concepts.

     



    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 Scalars, Vectors and Matrices, Hyper planes and HalfSpaces. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    2 Vector and Matrix PNorms (P=1,2,(), Solving Linear Equations and Nonlinear Equations. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    3 Matrix Inverses, NDimensional Functions: Regular and Contour Plots. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    4 Regular and Partial Derivatives, Gradient Vector and Hessian Matrix. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    5 Quadratic Forms, Convex and Concave Functions, Convex Regions. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    6 Optimality Conditions for Unconstrained Problems. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    7 KarushKuhnTucker (KKT or KT) Conditions and their Geometry. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    8 Midterm -
    9 Solutions of an LP problem: Simplex Method Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    10 Unconstrained Problems. Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    11 Nonlinear optimization problems Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    12 Nonlinear optimization problems Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    13 Lagrange multipliers Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    14 Project Presentations Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    15 Project Presentations Rao, S.S. (1984). “Optimization Theory and Application”. Wiley Eastern Ltd., New Delhi.
    16 Final Exam

     

    Course Notes/Textbooks Handouts prepared by the lecturer and some extracts above and exercises will be given.
    Suggested Readings/Materials Convex Optimization by Stephen Boyd and Lieven Vandenberghe , 2004.

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    Presentation / Jury
    1
    10
    Project
    1
    20
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    30
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    3
    60
    Weighting of End-of-Semester Activities on the Final Grade
    1
    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
    16
    5
    80
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    0
    Presentation / Jury
    1
    10
    10
    Project
    1
    15
    15
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    32
    32
    Final Exam
    1
    40
    40
        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.
    -
    -
    -
    -
    -
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    -
    -
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.
    -
    -
    -
    -
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    -
    -
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    -
    -
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs
    -
    -
    -
    -
    -
    7 Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    -
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    -
    -
    -
    -

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

     


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