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Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| COMPUTER SCIENCES PR. | |
| Code | BBZ406 |
| Name | Optimization Methods |
| Term | 2026-2027 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | VK Vocational Knowledge Courses E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. GÜZİN YÜKSEL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of the optimization course is to enable students to make optimal decisions using optimization techniques, to apply mathematical modeling and solution methods, and to learn the necessary tools and techniques to solve optimization problems.
Course Content
This course covers unconstrained and constrained optimization, and addresses the analytical solution of optimization problems, numerical methods, algorithms, equality constraints, equality and inequality constraints, and optimization under particular constraints.
Course Precondition
There are no prerequisites.
Resources
Prof. Dr. Özlem Türkşen, Optimizasyon Yöntemleri ve Matlab, Python, R Uygulamaları, Nobel, 2023. Nurhan Karaboğa, Optimizasyon Yöntemleri ve Matlab Uygulamaları , Nobel, 2023.
Notes
[1] Studies in Optimization First Edition D.M. Burley [2] M.A. Bhatti, Practical Optimization Methods, with Mathematica Applications, Springer-Verlag New York, Inc., 2000. [3]R. Fletcher, Practical Methods of Optimization, Second Edition, John-Wiley and Sons Ltd., Chichester, New York, 1987.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Identifies the concept and fundamental principles of optimization. |
| LO02 | Analyzes optimization problems by relating them to mathematical modeling. |
| LO03 | Classifies optimization problems. |
| LO04 | Identifies the concepts of objective function, constraints, and decision variables. |
| LO05 | Distinguishes between linear and nonlinear optimization problems. |
| LO06 | Chooses the most appropriate optimization technique for a particular problem. |
| LO07 | Applies optimization knowledge effectively in professional settings. |
| LO08 | Utilizes metaheuristic methods for solving complex optimization problems. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gain comprehensive knowledge of fundamental concepts, algorithms, and data structures in Computer Science. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learn essential computer topics such as software development, programming languages, and database management | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understand advanced computer fields like data science, artificial intelligence, and machine learning. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquire knowledge of topics like computer networks, cybersecurity, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develop skills in designing, implementing, and analyzing algorithms | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gain proficiency in using various programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learn skills in data analysis, database management, and processing large datasets. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Acquire practical experience through working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthen teamwork and communication skills. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Foster a mindset open to technological innovations. | 2 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourage the capacity for continuous learning and self-improvement. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Enhance the ability to solve complex problems | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Definition and basic concepts of optimization. | Reading sources. | Öğretim Yöntemleri: Beyin Fırtınası, Soru-Cevap, Anlatım |
| 2 | Linear and nonlinear optimization models. | Reading sources. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 3 | Mathematical formulation of optimization problems. | Reading sources. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Classical optimization techniques-1 | Reading sources. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Classical optimization techniques-2 | Reading sources. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 6 | Classical optimization techniques-3 | Reading sources. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Nonlinear programming: Unconstrained optimization techniques-1 | Reading sources. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 8 | Mid-Term Exam | Reading reference materials and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Nonlinear programming: Unconstrained optimization techniques-2 | Reading sources. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 10 | Nonlinear programming: Unconstrained optimization techniques-3 | Reading sources. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Nonlinear Programming: Constrained Optimization Techniques-1 | Reading sources. | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 12 | Nonlinear Programming: Constrained Optimization Techniques-2 | Reading sources. | Öğretim Yöntemleri: Soru-Cevap, Alıştırma ve Uygulama |
| 13 | Metaheuristic optimization techniques-1 | Reading sources. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 14 | Metaheuristic optimization techniques-2 | Reading sources. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Applications. | Review the solved problems. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 16 | Term Exams | Reading reference materials and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading reference materials and lecture notes. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 7 | 7 |
| Total Workload (Hour) | 125 | ||
| Total Workload / 25 (h) | 5,00 | ||
| ECTS | 5 ECTS | ||