BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| MECHATRONICS ENGINEERING (ENGLISH, NONTHESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE5010 | Optimization | Fall | 3 | 0 | 3 | 9 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Prof. Dr. SÜREYYA AKYÜZ |
| Course Lecturer(s): |
Prof. Dr. SÜREYYA AKYÜZ |
| Recommended Optional Program Components: | None |
| Course Objectives: | To equip students with the mathematical theory of optimization and solution methods. |
Learning Outcomes
|
The students who have succeeded in this course; Students will - be able to formulate optimization problems - understand basic differences between various constraints - apply numerical techniques to solve optimization problems |
Course Content
| Optimization as a decision making problem. Optimization over an open set. Optimization under equality constraints; Lagrange multipliers. Optimization under inequality constraints. Linear programming. Numerical methods. |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | The Optimization Problem. Examples. | |
| 2) | Mathematical preliminaries | |
| 3) | Mathematical preliminaries | |
| 4) | The Weierstrass Theorem. Application to example problems. | |
| 5) | Optimization over an open set. Necessary and sufficient conditions. | |
| 6) | Numerical techniques: Gradient algorithm, Newton's method. | |
| 8) | Optimization with equality constraints. Lagrange multipliers. | |
| 9) | Optimization with inequality constraints. Kuhn-Tucker conditions. | |
| 10) | Linear programming: Standard maximization and minimization problems. | |
| 11) | Linear programming: Primal and dual problems. Duality theorem. Optimality conditions. | |
| 12) | The Simplex algorithm. | |
| 13) | Discrete dynamic programming. | |
| 14) | Large optimization problems. Decomposition methods. |
Sources
| Course Notes / Textbooks: | 1. P. Varaia, Lecture Notes on Optimization, web |
| References: | 1. C.T. Kelley, Iterative Methods for Optimization, SIAM |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 5 | % 25 |
| Midterms | 1 | % 25 |
| Final | 1 | % 50 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 16 | 136 |
| Homework Assignments | 5 | 10 |
| Midterms | 1 | 2 |
| Final | 1 | 2 |
| Total Workload | 192 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |