Course Information

Course Title	Code	Semester	L+U Hour	Credits	ECTS
OPTIMIZATION	UAKT305	5. Semester	2 + 2	3.0	5.0

Prerequisites

None

Language of Instruction	Turkish
Course Level	Bachelor's Degree
Course Type	Compulsory
Mode of delivery	Computer aided oral presentation
Course Coordinator
Instructors	Gültaç EROĞLU İNAN
Assistants
Goals	Founding theoretical background necessary for undergraduate and graduate education, to take students modeling and analyzing a linear programming problem in their jobs public or private.
Course Content	Convex, concav sets, convex and concav functions, classic optimization, discriminant method, Newton-Raphson method, Jacobian method, Lagrange method, Kuhn-Tucker conditions, modelling of nonlinear programming problems, one-variable optimization, three-point interval search method, dichotomous search method, golden section method, Fibonacci method, quadratic programming, convex programming
Learning Outcomes	1) Defines several concepts related to the optimization theory. 2) Models nonlinear programming problems. 3) Prepares proper nonlinear programming model for a specific problem. 4) Defines proper optimization methods. 5) Compares the optimization algorithms for performance evaluation. 6) Makes analysis and interpretation by using software programs which is available for all decision makers.

Week	Topics	Teaching and Learning Methods and Techniques	Study Materials
1. Week	Convex and concave sets, convex and concave functions	Lecture; Question Answer; Problem Solving	Homework
2. Week	Classical optimization	Lecture; Question Answer; Problem Solving	Homework
3. Week	Discriminant method	Lecture; Question Answer; Problem Solving Brainstorming	Homework
4. Week	Newton-Raphson method	Lecture; Question Answer; Problem Solving	Homework
5. Week	Jacobian method and Lagrange method	Lecture; Question Answer; Problem Solving	Homework
6. Week	Khun-Tucker conditions	Lecture; Question Answer; Problem Solving	Homework
7. Week	Modeling of nonlinear programming problems	Lecture; Question Answer; Problem Solving	Homework
8. Week	Midterm exam	Question Answer
9. Week	Optimization with single variable	Lecture; Question Answer; Problem Solving	Homework
10. Week	Three point interval search method	Lecture; Question Answer; Problem Solving	Homework
11. Week	Bisection search method	Lecture; Question Answer; Problem Solving	Homework
12. Week	Golden section method	Lecture; Question Answer; Problem Solving	Homework
13. Week	Fibonacci method	Lecture; Question Answer; Problem Solving	Homework
14. Week	Quadratic programming and concave programming	Lecture; Question Answer; Problem Solving	Homework
15. Week	Preparation for final exam	Lecture; Question Answer; Problem Solving	Homework
16. Week	Final exam	Question Answer

Recommended Sources

Apaydın, A. (1996). Optimizasyon , A.Ü. Döner Sermaye Yayınları, Ankara.

Bazara, M.S., Shefty, C.M. (1979). Nonlinear Programming, Theory and Algorithms , John Wiley&Sons, New York.

Rao, S.S. (1991). Optimization Theory and Application , USA.

*DK = Course's Contrubution.

	0	1	2	3	4	5
Level of contribution	None	Very Low	Low	Fair	High	Very High

Event	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Total weeks*Hours per week)	14	4	56
Work Hour outside Classroom (Preparation, strengthening)	14	4	56
Homework	3	2	6
Midterm Exam	1	2	2
Time to prepare for Midterm Exam	1	12	12
Final Exam	1	2	2
Time to prepare for Final Exam	1	18	18
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course			30