Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
YMB0029		Optimization	3/0/0	DE	Turkish	7

Course Goals	To give the students a working knowledge of optimization theory and methods and to equip the students with sufficient backround for further study of advanced topics in optimization.
Prerequisite(s)	-
Corequisite(s)	-
Special Requisite(s)	The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)	Assist. Prof. Dr. Hikmet Çağlar, Assist. Prof. Dr. Fatih Uçar
Course Assistant(s)
Schedule	Day, hours, XXX Campus, classroom number.
Office Hour(s)	Instructor name, day, hours, XXX Campus, office number.
Teaching Methods and Techniques	Lecture and laboratory recitation
Principle Sources	Edwin K. P. Chong and Stanislaw H. Żak, An Introduction to Optimization, Second Edition, Wiley-Interscience Series in Discrete Mathematics and Optimization, John Wiley & Sons, Inc., New York, ©2001.
Other Sources	M. Asghar Bhatti, Practical optimization methods: with Mathematica Applications, Springer, New York, ©2000.

Course Schedules
Week	Contents	Learning Methods
1. Week	Introduction to Optimization	Lecture and laboratory recitation
2. Week	Convex Analysis	Lecture and laboratory recitation
3. Week	Convex Analysis	Lecture and laboratory recitation
4. Week	Constrained Optimization: Unconstrained Optimization, Problems with Equality Constraints, Problems with Inequality and Equality Constraints	Lecture and laboratory recitation
5. Week	Constrained Optimization: Unconstrained Optimization, Problems with Equality Constraints, Problems with Inequality and Equality Constraints	Lecture and laboratory recitation
6. Week	Computational Methods	Lecture and laboratory recitation
7. Week	Computational Methods	Lecture and laboratory recitation
8. Week	Midterm	Lecture and laboratory recitation
9. Week	Computational Methods	Lecture and laboratory recitation
10. Week	Computational Methods	Lecture and laboratory recitation
11. Week	Optimization in Application Area	Lecture and laboratory recitation
12. Week	Optimization in Application Area	Lecture and laboratory recitation
13. Week	Optimization in Application Area	Lecture and laboratory recitation
14. Week	Optimization in Application Area	Lecture and laboratory recitation
15. Week
16. Week
17. Week

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	40
Homework / Term Projects / Presentations	2	20
Final Exam	1	40

Program Outcomes PO-1 Have scientific research in mathematics and computer science in the level of theoretical and practical knowledge. PO-2 On the basis of undergraduate level qualifications, develop and deepen the same or a different areas of information at the level of expertise, and analyze and interpret by using statistical methods PO-3 Develop new strategic approaches for the solution of complex problems encountered in applications related to the field and unforeseen and take responsibility for the solution. PO-4 Evaluate critically skills acquired in the field of information in the level of expertise and assess the learning guides. PO-5 Transfer current developments in the field and their work to the groups inside and outside the area supporting with quantitative and qualitative datas as written, verbal and visual by a systematic way. PO-6 Use information and communication technologies with computer software in advanced level. PO-7 Develop efficient algorithms by modeling problems faced in the field and solve such problems by using actual programming languages. PO-8 Respect to social, scientific, cultural and ethical values at the stages of data collection related to the field, interpretation, and implementation. PO-9 To solve problems related to the field, establish functional interacts by using strategic decision making processes. PO-10 Establish and discuss in written, oral and visual communication in an advanced level by using at least one foreign language.

Learning Outcomes LO-1 I. Apply konvex analysis. LO-2 II. Solve constrained and unconstrained optimization problems LO-3 III. Solve Lagrange dual problem. LO-4 IV. Apply optimization to computational methods.

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10