PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
EKNM 307	OPTIMIZATION	3 + 0	5th Semester	5

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
2	Knows the classical optimization methods.
3	Klasik optimizasyon yöntemlerini bilir.
4	Non-linear functions, min / max points, finds.
5	Under the constraint functions are non-linear min / max points, finds.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11
LO 001		3	3	3		3	3			3
LO 002		3	3	3		3	3			3
LO 003		3	3	3		3	3			3
LO 004		3	3	3		3	3			3
LO 005		3	3	3		3	3			3
Sub Total		15	15	15		15	15			15
Contribution	0	3	3	3	0	3	3	0	0	3	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	4	56
Mid-terms	1	15	15
Final examination	1	17	17
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2019-2020 Fall	2	ATALAY ÇAĞLAR

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
EKNM 307	OPTIMIZATION	3 + 0	2	Turkish	2019-2020 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. ATALAY ÇAĞLAR	acaglar@pau.edu.tr		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.

Topics

Weeks	Topics
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

Resources	Resources 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, 1996	English
Applied Optimization with MATLAB Programming, P. Venkataraman, Wiley Interscience, NewYork, 2002.	English
Optimizasyon, Ayşen Apaydın, A.Ü., Ankara, 2005.	Türkçe

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	60	Final Exam
Midterm Exam	40	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student