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 06
LO 01333333
LO 02333333
LO 03333333
LO 04333333
LO 05333333
Sub Total151515151515
Contribution333333

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14570
Mid-terms144
Final examination11414
Total Work Load

ECTS Credit of the Course






130

5

COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2019-2020 Fall2ATALAY ÇAĞLAR

Course Details
Course Code:  EKNM 307 Course Title:  OPTIMIZATION
L+P Hour : 3 + 0   Course Code : 2   Language Of Instruction: Turkish Course Semester :  2019-2020 Fall
Course Coordinator :  ASSOCIATE PROFESSOR ATALAY ÇAĞLAR E-Mail:  acaglar@pau.edu.tr Phone Number : 
Course Location İİBF C0303,
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.
Attendance : %
Topics
WeeksTopics
1 Introduction to Optimization
2 Classical Optimization Methods: Single Variable, Unconstrained Multivariate Optimization Methods
3 Multivariate Optimization Methods with Equality Costraints : Direct Replacement Methods, Lagrange Multipliers Methods
4 Multivariate Optimization Methods with Inequality Costraints: Lagrange Multipliers Methods, Kuhn-Tucker Conditions
5 Nonlinear Programming (Single Variable Optimization) : Unrestricted Search, Exhaustive Search
6 Dichotomous Search, Golden Section Methods, Fibonacci Methods, etc.
7 Nonlinear Programming (Unconstrained Multivariate Optimization) : Direct Search Methods, Univariate Method,
8 Midterm
9 Hook-Jeeves Method
10 Powel Method, Rosenbrock Methods
11 Gradient Methods: Steepest Ascent Methods, Steepest Descent Methods
12 Newton Method (Single Variable and Multivariable) , Fletcher-Reeves Method
13 Nonlinear Programming (Constrained Multivariate Optimization), Direct Methods, Zoutendijk Method
14 Indirect Methods: Penalty Function 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
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
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