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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Electrical and Electronics Engineering (Without Thesis)

    EEE 515 | Course Introduction and Application Information

    Course Name
    Introduction to Convex Optimization
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    EEE 515
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second / Third Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator
    Course Lecturer(s)
    Assistant(s) -
    Course Objectives This course aims to teach the tools and train the students to recognize convex optimization problems that arise in scientific and engineering applications. Besides, it aims to present the basic theory, and concentrate on modeling aspects and results that are useful in applications.
    Learning Outcomes
    #
    Content
    		 PC Sub
    * Contribution Level
    1
    2
    3
    4
    5
    1Determine the convex sets, cones and functions
    2Formulate convex optimization problems
    3Determine the dual problems
    4Develop skills to solve common problems in
    5Implement convex optimization problems and methods in MATLAB based on a given algorithmic description or theory
    Course Description Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.

     



    Course Category

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

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Introduction to mathematical optimization; least-squares and linear programming; convex optimization; course goals and topics. CH1, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 052183378
    2 Overview of linear algebra Linear Algebra and its Applications, Gill Strang, Cengage Learning ISBN-10: 0534422004
    3 Convex sets and cones. CH2, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    4 Common and important examples; operations that preserve convexity. CH2, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    5 Convex functions CH3, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    6 Common examples; operations that preserve convexity; quasiconvex and log-convex functions. CH3, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    7 Convex optimization problems, linear and quadratic programs; CH4, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    8 Second-order cone and semidefinite programs; quasiconvex optimization problems; CH4, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    9 Duality, Lagrange dual function and problem CH5, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    10 Optimality conditions CH5, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    11 Applications: approximation and fitting; Norm approximation; regularization; robust optimization CH6, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    12 Applications: statistical estimation; Maximum likelihood and MAP estimation; detector design. CH7, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    13 Applications: geometric problems; Projection; extremal volume ellipsoids; placement and location problems. CH8, Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    14 Presentation of term projects to class
    15 Presentation of term projects to class
    16 Presentation of term projects to class

     

    Course Notes/Textbooks
    1. Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press, ISBN-10: 0521833787
    2. Numerical Optimization, J. Nocedal and S. Wright, Springer Series in Operations Research, ISBN-13: 978-0-387-22742-9
    3. Linear Algebra and its Applications, Gill Strang, Cengage Learning  ISBN-10: 0534422004
    Suggested Readings/Materials
    1. Lectures on Modern Convex Optimization, A. Ben-Tal and A. Nemirovski, MPS-SIAM Series on Optimization, ISBN-10: 0898714915
    2. Nonlinear Programming, D. Bertsekas, Athena Scientific, ISBN-10: 1886529000

     

    EVALUATION SYSTEM

    Semester Activities Number Weighting LO 1 LO 2 LO 3 LO 4 LO 5
    Participation
    1
    10
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    1
    50
    Presentation / Jury
    Project
    1
    40
    Seminar / Workshop
    Oral Exams
    Midterm
    Final Exam
    Total

    Weighting of Semester Activities on the Final Grade
    3
    100
    Weighting of End-of-Semester Activities on the Final Grade
    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
    3
    42
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    6
    10
    60
    Presentation / Jury
    0
    Project
    1
    75
    75
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    0
    Final Exam
    0
        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 Electrical and Electronics Engineering, evaluates, interprets and applies information.
    -
    -
    -
    X
    -
    2

    Is well-informed about contemporary techniques and methods used in Electrical and Electronics 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 Electrical and Electronics 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.
    -
    -
    -
    -
    -
    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 Electrical and Electronics engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics 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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