Erciyes University - Info Package

Course unit title	Level of course unit	Course unit code	Type of course unit	Semester of course unit	Local credit	ECTS credit	Syllabus
MATHEMATICAL PROGRAMMING	Third cycle	İŞL 641		1	7.00	7.00	Print

Description of course unit

Prerequisites and course requisities	None
Language of instruction	Turkish
Coordinator	ASST.PROF.DR. AHMET HASKÖSE
Lecturer(s)	ASST.PROF.DR. AHMET HASKÖSE
Teaching assitant(s)	None
Mode of delivery	Lectures, student-based autonomous study and class participation of the students
Course objective	To develop knowledge of the mathematical structure of the most commonly used deterministic optimization models and ability to analyze the structure of mathematically model various complex systems occurring in business applications.
Course description	Topics include applications of advanced models and methods in Linear Programming, Nonlinear Programming problems, constrained and unconstrained problems

Course contents

1	Review of Modeling
2	Large-scale Linear Programming and Network Flow Models
3	Applications of advanced linear programming
4	Case study
5	Nonlinear Programming problem classification and examples
6	Optimality conditions for unconstrained problems
7	Optimality conditions for constrained problems
8	Mid-term exam
9	Kuhn-Tucker (K-T) conditions
10	Kuhn-Tucker (K-T) conditions
11	Algorithms for unconstrained optimization
12	Algorithms for unconstrained optimization
13	Nonlinear Programming applications
14	Case study
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20

Learning outcomes of the course unit

1	To have knowledge on analysis of the systems through modelling tools
2	To have knowledge about advanced linear programming models
3	To have knowledge about nonlinear programming and its applications
4	To have knowledge about various algorithms for constrained and unconstrained optimization
5	Having knowledge about various constrained optimization algorithms
6	Having knowledge about various unconstrained optimization algorithms
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10

*Contribution level of the course unit to the key learning outcomes

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Number of stars refer to level of contribution from 1 (the least) to 5 (the most)

Planned learning activities, teaching methods and ECTS work load

	Quantity	Time (hour)	Quantity*Time (hour)
Lectures (face to face teaching)	14	3	42
Study hours out of classroom (study before and after the class)	10	2	20
Homework	2	5	10
Presentation / seminar	2	12	24
Quiz	0	0	0
Preparation for midterm exams	1	10	10
Midterm exams	1	2	2
Project (term paper)	0	0	0
Laboratuar	0	0	0
Field study	0	0	0
Preparation for final exam	1	10	10
Final exam	1	2	2
Research	10	3	30
Total work load			150
ECTS			6.00

Assessment methods and criteria

Evaluation during semester	Quantity	Percentage
Midterm exam	1	100
Quiz	0	0
Homework	0	0
Semester total		100
Contribution ratio of evaluation during semester to success		40
Contribution ratio of final exam to success		60
General total		100

Recommended and required reading

Textbook	Sinha S.M., Mathematical Programming: Theory and Methods, Elsevier Science & Technology, 2006 Griva I., Nash S.G. and Sofer A., Linear and Nonlinear Optimization, the Society for Industrial and Applied Mathematics, 2009 Biegler L.T., Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes, the Society for Industrial and Applied Mathematics and the Mathematical Optimization Society, 2010 Nocedal J and Wright S.J., Numerical Optimization, Springer Media, LLC., 2006 Ahuja R.K., Magnanti, T.L. and Orlin J.B., Network Flows: Theory, Algorithms, and Applications, Prentice-Hall, Inc., 1993
Additional references	Related articles

Files related to the course unit