PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ISY 665	OPTIMIZATION	3 + 0	1st Semester	10

COURSE DESCRIPTION

Course Level	Doctorate Degree
Course Type	Elective
Course Objective	The aim of this course is to define the basic concepts of optimization, to model optimization problems, to examine the solution methods of these models; interpreting and applying various optimization models in different fields to gain theoretical and practical skills.
Course Content	Constrained and unconstrained optimization, graphical optimization, linear and nonlinear optimization techniques, direct and indirect methods, parametric programming
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Learns the basic concepts of optimization.
2	Model optimization problems.
3	Examines and interprets the solution methods of these models.
4	Applies various optimization models in different areas.

COURSE'S CONTRIBUTION TO PROGRAM

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	5	70
Assignments	14	3	42
Mid-terms	1	28	28
Final examination	1	30	30
Presentation / Seminar Preparation	3	16	48
Total Work Load ECTS Credit of the Course			260 10

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2020-2021 Fall	1	AYŞEGÜL TUŞ

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ISY 665	OPTIMIZATION	3 + 0	1	Turkish	2020-2021 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. AYŞEGÜL TUŞ	atus@pau.edu.tr		İİBF B0009	%70

Goals		The aim of this course is to define the basic concepts of optimization, to model optimization problems, to examine the solution methods of these models; interpreting and applying various optimization models in different fields to gain theoretical and practical skills.
Content		Constrained and unconstrained optimization, graphical optimization, linear and nonlinear optimization techniques, direct and indirect methods, parametric programming

Topics

Weeks	Topics
1	Introduction to optimization and basic concepts
2	Classical optimization: mathematical representations
3	Univariate Convex Functions and Properties
4	Derivative Independent Optimization Algorithms for Univariate Unconstrained Problems
5	Multivariate Convex Functions and Properties
6	Equation constrained multivariate optimization (direct replacement, Lagrange method)
7	Inequality constrained multivariate optimization (Kuhn-Tucker method)
8	Midterm
9	Linear optimization
10	Graphical optimization
11	Nonlinear optimization
12	Solution of Nonlinear Equations (Newton-Raphson Method)
13	Parametric programming
14	Applications

Materials

Materials are not specified.

Resources

Resources	Resources Language
APAYDIN, A. (2005) “Optimizasyon” Kılavuz Kitabevi 	Türkçe

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

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

Prospective Student