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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
EKNM 307OPTIMIZATION3 + 05th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective Obtaining the best result under optimization conditions. This course aim is to teach fundamental concepts and methods
Course Content Introduction to Optimization, Classical Optimization Methods, Nonlinear Programming: Single Variable, Unconstrained Multivariate Optimization Methods, Constrained Multivariate Optimization Methods.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1.Knows to get the best result under the given conditions
2Knows the classical optimization methods.
3Klasik optimizasyon yöntemlerini bilir.
4Non-linear functions, min / max points, finds.
5Under the constraint functions are non-linear min / max points, finds.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
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LO 002544455555555
LO 003555555555555
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Sub Total252323232424242525252525
Contribution555555555555

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)13452
Hours for off-the-classroom study (Pre-study, practice)13452
Mid-terms11313
Final examination11313
Total Work Load

ECTS Credit of the Course






130

5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2020-2021 Fall1ATALAY ÇAĞLAR


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
EKNM 307 OPTIMIZATION 3 + 0 1 Turkish 2020-2021 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. ATALAY ÇAĞLAR acaglar@pau.edu.tr İİBF AB112 %
Goals Obtaining the best result under optimization conditions. This course aim is to teach fundamental concepts and methods
Content Introduction to Optimization, Classical Optimization Methods, Nonlinear Programming: Single Variable, Unconstrained Multivariate Optimization Methods, Constrained Multivariate Optimization Methods.
Topics
WeeksTopics
1 Introduction to Optimization
2 Graphical Method
3 Classical Optimization Methods: Single Variable Optimization
4 Two Variables Optimization
5 Unconstrained Multivariate Optimization Methods: Matrices, Quadratic Form
6 Unconstrained Multivariate Optimization Methods, Gradient Vector, Hessien Matrix
7 Multivariate Optimization Methods with Equality Costraints : Direct Replacement Methods
8 Midterm
9 Multivariate Optimization Methods with Equality Costraints : Lagrange Multipliers Methods
10 Multivariate Optimization Methods with Inequality Costraints: Kuhn-Tucker Conditions
11 Nonlinear Programming (Single Variable Optimization) : Unrestricted Search, Exhaustive Search, Fixed Search
12 Two Symetrical Points Search, Split Search
13 Golden Section Methods, Fibonacci Methods,
14 Gradient Methods: Steepest Ascent Methods, Steepest Descent Methods
Materials
Materials are not specified.
Resources
ResourcesResources Language
Optimizasyon Teknikleri, Hasan Bal, Gazi Üniversitesi, Ankara, 1995.Türkçe
Doğrusal Olmayan Programlama, Gülsüm Oral, Akademi Matbaası, Ankara, 1989.Türkçe
Engineering Optimization: Theory and Practice, Singiresu S. Rao, Wiley Interscience, 1996English
Applied Optimization with MATLAB Programming, P. Venkataraman, Wiley Interscience, NewYork, 2002.English
Optimizasyon, Ayşen Apaydın, A.Ü., Ankara, 2005.Türkçe
Optimizasyon ve Matlab Uygulamaları, Aysun Tezel Özturan, Nobel Akademik Yayıncılık, 1. Baskı, Ankara, 2019Türkçe
Optimizasyon Problemlerinin R İle Çözümü, Namık Kemal Erdoğan, Nisan Kitabevi, Eskişehir, 2016.Türkçe
Nonlinear Programming: Theory and Algorithms, Mokhtar S. Bazaraa Hanif D. Sherali C. M. Shetty, Wiley, Third Edition, 2006.English
Taha, H. A., 1992, Yöneylem Araştırması, (Operations Research: An Introduction, 5th ed. Macmillan, New York.) Literatür, İstanbul, 2000.Türkçe
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam60Final Exam
Midterm Exam40Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes