TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES |
| ECONOMETRICS PR. (ENGLISH) | |
| Code | ECMS305 |
| Name | Optimization |
| Term | 2024-2025 Academic Year |
| Semester | 5. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. SÜLEYMAN BİLGİN KILIÇ |
| Course Instructor |
Dr. Öğr. Üyesi Salih ÇAM
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To give analytical thinking and problem-solving skills to the students by means of learning and application of the optimization methods
Course Content
Introduction to the theory of optimization, necessary and sufficient conditions for the global maximum or minimum of unrestricted functions, Newton-Raphson method, constrained optimization of continuous functions, lagrangian method in the form of equality constraints, linear programming and the simplex method, sensitivity analysis, expansion of the Lagrangian method in the form inequality constraints, determination of the Kuhn-Tucker necessary and sufficient conditions for the non-linear constrained problems, non-linear programming algorithms; gradient descent method, quadratic programming, geometric programming and stochastic programming
Course Precondition
No prerequisites
Resources
Winston, W. L. (1991). Operations research: applications and algorithms. Taha, H. A., & Taha, H. A. (1997). Operations research: an introduction (Vol. 7). Upper Saddle River, NJ: Prentice hall. Hillier, F. S., & Lieberman, G. J. (2015). Introduction to operations research. McGraw-Hill.
Notes
related statistical and econometric packages
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Gains analytical thinking and problem solving skills |
| LO02 | Construct the mathematical models |
| LO03 | Knows the basic concepts of optimization. |
| LO04 | Obtains, edits and analyzes the data |
| LO05 | Use an econometric software |
| LO06 | be able to model and analyze complex optimization problems |
| LO07 | Expresses problems in mathematical form. |
| LO08 | Solves mathematical functions with appropriate algorithms |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the basic concepts and theorems in the fields of Econometrics, Statistics and Operations research | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires basic Mathematics, Statistics and Operation Research concepts | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Describes the necessary concepts of Business | 4 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Equipped with the foundations of Economics, and develops Economic models | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Models problems with Mathematics, Statistics, and Econometrics | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Analyzes/interprets at the conceptual level to develop solutions to problems | 1 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Collects/analyses data from reliable data sources for the purpose of study | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Interprets the results analyzed with the model | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Combines the information obtained from different sources within the framework of academic rules in a field of research | 4 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Adapts traditional approaches, practices and methods to a new study when necessary | 5 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses a package program of Econometrics, Statistics, and Operation Research | 3 |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Leads by taking responsibility individually and/or within the team | 1 |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | In addition to herself/himself professional development, constantly improves in scientific, cultural, artistic and social fields in line with interests and abilities | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the necessity of lifelong learning, it follows the current developments in the field / constantly renews itself | |
| PLO15 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses Turkish and at least one other foreign language, academically and in the business context | |
| PLO16 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Interprets the feelings, thoughts and behaviors of the related persons correctly/expresses himself/herself correctly in written and verbal form | |
| PLO17 | Yetkinlikler - Alana Özgü Yetkinlik | Interprets data on current economic and social issues | |
| PLO18 | Yetkinlikler - Alana Özgü Yetkinlik | Applies social, scientific and professional ethical values |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to the theory of optimization | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 2 | Necessary and sufficient conditions for the global maximum or minimum of unrestricted functions | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 3 | Newton-Raphson method | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 4 | Constrained optimization of continuous functions | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 5 | Lagrangian method in the form of equality constraints | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 6 | Linear programming and the simplex method | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | The sensitivity analysis | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Soru-Cevap, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Preparation for the exam. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Expansion of the Lagrangian method in the form inequality constraints | Preparation for the exam. | Öğretim Yöntemleri: Anlatım |
| 10 | Determination of the Kuhn-Tucker necessary and sufficient conditions for the non-linear constrained problems | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 11 | Non-linear programming algorithms | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 12 | Gradient descent method | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 13 | Quadratic programming | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım |
| 14 | Geometric programming | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Stochastic programming | It is prepared by examining the relevant topics from the weekly program. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama, Problem Çözme |
| 16 | Term Exams | Preparation for the exam. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the exam. | Ölçme Yöntemleri: Yazılı Sınav |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | ||