MATH 553 | Course Introduction and Application Information - Applied Mathematics and Statistics – With Thesis

Course Name

Optimization

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 553	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Q&A Lecture / Presentation
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	Prof. Dr. Gözde Yazgı TÜTÜNCÜ
Assistant(s)	Araş. Gör. Merve KAHRAMAN ARİMAN

Course Objectives	Upon the completion of this course the student expected to gain an aiding tool for solving realworld problems which require LP approach, be able to have a detailed information about fundamental methods of LP such as Graphical method and the simplex method. Students will have detailed information about Duality and Sensitivity of L.P's. They will have information on unconstraint and nonlinear optimization models and methods to solve this problem.
Learning Outcomes	The students who succeeded in this course; will be able to model real life problems using Linear programming(LP). will be able to solve problems using methods of LP such as Graphical method, the simplex method and BigM method.. will be able to analyze dual problem and marginal costs using Duality theorem. will be able to inspect optimization problems with Sensitivity analysis. will be able to develop unconstraint and nonlinear optimization models. will be able to solve unconstraint and nonlinear optimization problems.
Course Description	Linear Programming: Modeling, Solution Methods, Duality in linear programming; Nonlinear programming: First and second order optimality conditions for unconstrained problems, Lagrange multipliers, convexity in mathematical programming, The KuhnTucker theorem; Discrete optimization.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Basic Linear Algebra	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
2	Introduction to Linear Programming	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
3	The Simplex Algorithm	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
4	Sensitivity Analysis	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
5	Duality	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
6	Integer Programming	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
7	Advanced Topics in LP	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
8	Midterm
9	Nonlinear Programming	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
10	Nonlinear Programming	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
11	Probablistic Inventory Models	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
12	Game Theory	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
13	Project Presentations	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
14	Project Presentations	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
15	Semester Review	Operations Research Applications and Algorithms, W.L.Winston, 4th Edition , 2004. Introduction to Operations Research, 7th Edition, Hillier, Liberman, McGrawHill, 2001
16	Final Exam

Course Notes/Textbooks	"Operations Research Applications and Algorithms" by W.L.Winston, Duxbury Press 4th Edition , 1997, ISBN-13: 978-0534520205
Suggested Readings/Materials	"Introduction to Operations Research", 7th Edition, Hillier, Liberman, McGrawHill, 2001. ISBN-13: 978-0072461213

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury	1	20
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	20
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	10	6	60
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury	1	10	10
Project	1	20	20
Seminar / Workshop			0
Oral Exam			0
Midterms	1	32	32
Final Exam	1	55	55
		Total	225

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Program Competencies/Outcomes

* Contribution Level

1

2

3

4

5

1

To be able to demonstrate independent and critical thinking in Applied Mathematics and Statistics.

-

2

To be able to define problems in Applied Mathematics/Statistics and verify whether they are mathematically/statistically consistent.

-

X

-

3

To be able to analyse and solve real life problems using applied methods and interdisciplinary approach of Mathematics/Statistics.

-

X

4

To be able to independently conduct, conclude, and report on specialized research in Applied Mathematics and Statistics.

-

X

5

To be able to efficiently use national and international resources, for staying updated in the field, communicating with colleagues, and following the related literature.

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6

To be able to develop proficiency in using computer software widely utilized in the fields of Applied Mathematics and Statistics.

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7

To be able to evaluate solution processes efficiently using mathematical reasoning and modeling in order to contribute to the solutions of social and scientific problems.

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8

To be able to synthesize theoretical frameworks with practical applications through mathematical and statistical methods.

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9

To be able to develop strategies, policies and plans for problems and research areas in Applied Mathematics/Statistics in order to interpret the results and translate them into practice.

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10

To be able to translate key topics, events, and phenomena in Applied Mathematics and Statistics into the context of other scientific disciplines.

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11

To be able to engage in lifelong learning by continuously updating and improving knowledge and skills in Applied Mathematics and Statistics.

-

X

MATH 553 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

EVALUATION SYSTEM

ECTS / WORKLOAD TABLE

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IZMIR UNIVERSITY OF ECONOMICS GÜZELBAHÇE CAMPUS

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VALUING PEOPLE

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