ELE604 - OPTIMIZATION
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| OPTIMIZATION | ELE604 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| Course objective | It is aimed to give the following topics to the students; a) Recognising and classifying an optimisation problem, b) Tools for learning and analysing convex sets and functions, c) Basic algorithms used in solving convex optimisation problems, d) Duality concept in constrained problems and the techniques being used to apply them, mainly staying in the context of convex optimisation, so that they can solve problems which they may encounter with in their studies/projects. | |||||
| Learning outcomes |
| |||||
| Course Content | Brief reminder of linear algebra topics, Convexity, convex sets and functions, Gradiant Descent, Steepest Descent, Newton Algorithms and their variations for unconstrained problems, Constrained problems and Karush-Kuhn-Tucker Conditions, Modification of the above algorithms for unconstrained problems to constrained problems, Ä°nterior Point Algorithms (Penalty ve Barrier Methods) | |||||
| References | 1. Luenberger, Linear and Nonlinear Programming, Kluwer, 2002. 2. Boyd and Vandenberghe, Convex Optimization, Cambridge, 2004. 3. Baldick, Applied Optimization, Cambridge, 2006. 4. Freund, Lecture Notes, MIT. 5. Bertsekas, Lecture Notes, MIT. 6. Bertsekas, Nonlinear Programming, Athena Scientific, 1999 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Brief reminder of linear algebra topics |
| Week 2 | Brief reminder of linear algebra topics |
| Week 3 | Optimality conditions for unconstrained problems Convex Sets |
| Week 4 | Convex and concave functions Conditions for convexity Operations that preserve convexity |
| Week 5 | Quadratic functions, forms and optimization Optimality conditions Unconstrained minimization |
| Week 6 | Descent Methods Convergence |
| Week 7 | Algorithms: Gradient Descent Algorithm |
| Week 8 | Algorithms: Steepest Descent Algorithm |
| Week 9 | Algorithms: Newton?s Algorithm |
| Week 10 | Midterm Exam |
| Week 11 | Constrained optimization Duality |
| Week 12 | Optimality conditions, KKT Conditions Algorithms: Feasible Direction Method, Active Set Method |
| Week 13 | Algorithms: Gradient Projection Method, Newton?s Algorithm with Equality Constraints |
| Week 14 | Algorithms: Penalty and Barrier Methods |
| Week 15 | Study week |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 13 | 30 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 14 | 60 |
| Percentage of final exam contributing grade succes | 1 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 13 | 5 | 65 |
| Midterms (Study duration) | 1 | 29 | 29 |
| Final Exam (Study duration) | 1 | 34 | 34 |
| Total Workload | 43 | 76 | 240 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge. | X | ||||
| 2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering. | X | ||||
| 3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | X | ||||
| 4. Designs and runs research projects, analyzes and interprets the results. | X | ||||
| 5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. | X | ||||
| 6. Produces novel solutions for problems. | X | ||||
| 7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | X | ||||
| 8. Follows technological developments, improves him/herself , easily adapts to new conditions. | X | ||||
| 9. Is aware of ethical, social and environmental impacts of his/her work. | X | ||||
| 10. Can present his/her ideas and works in written and oral form effectively; uses English effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest