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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 |
The students who succeeded in this course;
|
| 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. |
|
|
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 |
|
| Suggested Readings/Materials |
|
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| 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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