GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
OPTIMIZATION TECHNIQUES I/İST-303
Course Title: OPTIMIZATION TECHNIQUES I
Credits 4 ECTS 7
Semester 5 Compulsory/Elective Compulsory
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Prof. Dr. Hasan BAL
 -- WEB SITE(S) OF LECTURER(S)
  http://websitem.gazi.edu.tr/site/hasanbal
 -- EMAIL(S) OF LECTURER(S)
  hasanbal@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
Gaining ability to express the real life problems as an optimization model
Learning the basic theorems of classical optimization
Learning the local minimum-maximum-global minimum-maximum points in an optimization problem
Learning the concepts of convex -concave functions
Obtaining enough knowledge to solve single and multiple variable optimization problems
Gaining ability to solve a multivariate optimization problem with equality constraints
Gaining ability to solve a multivariate optimization problem with inequality constraints
Obtaining enough knowledge to solve an unconstrained optimization problem with direct search methods
Gaining ability to solve an unconstrained optimization problem with gradient search methods
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  Operations Research I
 --COURSE CONTENT
1. Week  Fundamental concepts of Optimization and Operations research and reviewing of related mathematical background.
2. Week  Convex-concave functions and its importance in optimization
3. Week  Single-variable unconstrained optimization and basic theorems.
4. Week  Multivariable unconstrained optimization and basic theorems
5. Week  Constrained optimization, basic theorems, graphical solution
6. Week  Equality Constrained optimization models and graphics solutions
7. Week  Inequality Constrained optimization models and graphics solutions
8. Week  Midterm exam
9. Week  Kuhn-Tucker and Karush-Kuhn-Tucker methods
10. Week  Kuhn-Tucker and Karush-Kuhn-Tucker methods
11. Week  Quadratic programming and portfolio samples
12. Week  Search techniques (fixed-step, step should be increased two points)
13. Week  Search methods ( the golden ratio search, gradient search, powell search, hooke jeves search)
14. Week  Newton Methods
15. Week  Current Software Support Related to course content (WinQSB, Matlab, ...).
16. Week  Final exam
 -- RECOMMENDED OR REQUIRED READING
  Bal H., 1985, Optimizasyon Teknikleri, G.Ü. publishing. Rao S.S., 1991, Optimization : Theory application, second edition, Willey.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer
 -- WORK PLACEMENT(S)
  Uygulama
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
30
 Assignment
2
10
 Exercises
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
4
56
 Practising Hours of Course Per Week
0
 Reading
10
4
40
 Searching in Internet and Library
10
3
30
 Designing and Applying Materials
0
 Preparing Reports
0
 Preparing Presentation
0
 Presentation
0
 Mid-Term and Studying for Mid-Term
2
5
10
 Final and Studying for Final
4
10
40
 Other
0
 TOTAL WORKLOAD: 
176
 TOTAL WORKLOAD / 25: 
7.04
 ECTS: 
7
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. The statistical textbooks which include latest information about statistics, equipment and other resources supported by scientific approach on undergraduate level have theoretical and practical knowledge.X
22. Statisticians by using knowledge and skills acquired at bachelor degree level model, analyze, and interpret datasets.X
33. Statisticians identify and analyze the problems with current developments in statistic and also develop solutions based upon researches and proofs.X
44. Statisticians apply theoretical and practical knowledge acquired in Statistics at bachelor degree level to the current problems.X
55. Statisticians have the ability to use computer software and computing technology at the certain level required by statistics field.X
66. Statisticians take responsibility at disciplinary and interdisciplinary studies as an individual or a team member.X
77. Statisticians must have knowledge and ability to follow development in the field of Statistics, and must develop life long-learning attitudes.X
88. By using a foreign language, statistician can keep track of every statistical information, and communicate with colleagues.X
99. Applying the statistical knowledge in the professional sense, statistician has social, scientific, and ethical values.X
1010. A statistician must have the ability to social sensitivity and socialization.X
1111. During the process of inference, a statistician uses time efficiently with the analytical thinking ability.X