ISB208 Optimization Techniques 2

5 ECTS - 3-0 Duration (T+A)- 4. Semester- 3 National Credit

Information

Unit FACULTY OF SCIENCE AND LETTERS
STATISTICS PR.
Code ISB208
Name Optimization Techniques 2
Term 2026-2027 Academic Year
Semester 4. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 5 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Belirsiz
Type Normal
Label C Compulsory
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Doç. Dr. NİMET ÖZBAY
Course Instructor
The current term course schedule has not been prepared yet.


Course Goal / Objective

The aim of this course is to comprehend unconstrained and constrained multivariate optimization problems and their solution methods and to gain the ability to solve some nonlinear optimization problems.

Course Content

The content of this course consists of the following topics: Unconstrained Univariate Optimization, Unconstrained Bivariate Optimization, Unconstrained Multivariate Optimization, Multivariate Optimization with Equality Constraints, Jacobian Method, Lagrangian Method, Inequality Restricted Optimization, Kuhn-Tucker Conditions, Nonlinear Optimization Techniques.

Course Precondition

None

Resources

-Optimizasyon Teknikleri, Hasan Bal, Gazi Üniversitesi, Ankara, 1995. -A First Course in Optimization Theory, Rangarajan K. Sundaram, Cambridge University Press, 1996. -Doğrusal Programlama, İmdat Kara, Bilim Teknik Yayınevi, 1991. -Yöneylem Araştırması, Hamdy A. Taha, Literatür Yayıncılık, 2003. -Optimizasyon ve Matlab Uygulamaları, Aysun Tezel Özturan, Nobel Akademik Yayıncılık, 2022.

Notes

-Optimizasyon, Ayşen Apaydın, A.Ü., Ankara, 2005.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Uses unconstrained optimization problems and solution methods.
LO02 Uses the solution methods of unconstrained multivariate optimization problems.
LO03 Solves equality constrained multivariate optimization problems.
LO04 Has the ability to apply Jacobian and Lagrangian methods.
LO05 Solves inequality constrained optimization problems.
LO06 Writes the Kuhn-Tucker conditions.
LO07 Explains some nonlinear optimization methods and their relationships.
LO08 Solves some nonlinear optimization problems.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explain the essence fundamentals and concepts in the field of Statistics
PLO02 Bilgi - Kuramsal, Olgusal Emphasize the importance of Statistics in life 3
PLO03 Bilgi - Kuramsal, Olgusal Define basic principles and concepts in the field of Law and Economics
PLO04 Bilgi - Kuramsal, Olgusal Produce numeric and statistical solutions in order to overcome the problems
PLO05 Bilgi - Kuramsal, Olgusal Use proper methods and techniques to gather and/or to arrange the data 2
PLO06 Bilgi - Kuramsal, Olgusal Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization
PLO07 Bilgi - Kuramsal, Olgusal Apply the statistical analyze methods 3
PLO08 Bilgi - Kuramsal, Olgusal Make statistical inference (estimation, hypothesis tests etc.)
PLO09 Bilgi - Kuramsal, Olgusal Generate solutions for the problems in other disciplines by using statistical techniques and gain insight
PLO10 Bilgi - Kuramsal, Olgusal Discover the visual, database and web programming techniques and posses the ability of writing programs
PLO11 Beceriler - Bilişsel, Uygulamalı Distinguish the difference between the statistical methods
PLO12 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Make oral and visual presentation for the results of statistical methods 3
PLO13 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Have capability on effective and productive work in a group and individually 2
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 2
PLO15 Yetkinlikler - Öğrenme Yetkinliği Develop scientific and ethical values in the fields of statistics-and scientific data collection


Week Plan

Week Topic Preparation Methods
1 Unconstrained Univariate Optimization Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
2 Unconstrained Bivariate Optimization Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
3 Unconstrained Multivariate Optimization Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme
4 Multivariate Optimization with Equality Constraints Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama
5 Jacobian Method Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Lagrangian Method Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 Equality Constrained Multivariate Optimization Problems Source reading Öğretim Yöntemleri:
Problem Çözme
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
9 Sensitivity Analysis in Equality Constrained Multivariate Optimization Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
10 Elimination methods Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
11 Exhaustive Search, Dichotomous Search Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
12 Fibonacci Search Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
13 Golden Ratio Search Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme
14 Gradient Descent Source reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
15 Problem Solving Source reading Öğretim Yöntemleri:
Problem Çözme
16 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 3 42
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 12 12
Final Exam 1 18 18
Total Workload (Hour) 114
Total Workload / 25 (h) 4,56
ECTS 5 ECTS

Update Time: 04.05.2026 01:10