GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
OPTIMIZATION TECHNIQUES I/İST3003
Course Title: OPTIMIZATION TECHNIQUES I
Credits 4 ECTS 6
Course Semester 5 Type of The Course Compulsory
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
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
 --WEEKLY SCHEDULE
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, graphics solutions
6. Week  Equality Constrained optimization models and graphics solutions
7. Week  Inequality Constrained optimization models and graphics solutions
8. Week  Kuhn-Tucker and Karush-Kuhn-Tucker methods, Midterm exam
9. Week  Kuhn-Tucker and Karush-Kuhn-Tucker methods
10. Week  Quadratic programming and sample Portfolios
11. Week  Search techniques (fixed step, incremental step, two points)
12. Week  Search techniques (three points, golden ratio, fibonacci search)
13. Week  Newton's methods
14. Week  Current Topics Related to Software Support (WinQSB, Matlab,...)
15. Week  Final Exam
16. Week  --
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
30
 Assignment
2
10
 Application
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
40
 Percentage of Final Exam to Total Score  
60
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
4
56
 Weekly Tutorial Hours
0
 Reading Tasks
10
2
20
 Searching in Internet and Library
10
2
20
 Material Design and Implementation
0
 Report Preparing
0
 Preparing a Presentation
0
 Presentation
0
 Midterm Exam and Preperation for Midterm Exam
5
5
25
 Final Exam and Preperation for Final Exam
6
5
30
 Other (should be emphasized)
0
0
0
 TOTAL WORKLOAD: 
151
 TOTAL WORKLOAD / 25: 
6.04
 Course Credit (ECTS): 
6
 -- 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
 -- NAME OF LECTURER(S)
   (Prof. Hasan BAL , Prof. H. Hasan ÖRKCÜ)
 -- WEB SITE(S) OF LECTURER(S)
   (http://www.websitem.gazi.edu.tr/site/hasanbal , http://www.websitem.gazi.edu.tr/site/hhorkcu)
 -- EMAIL(S) OF LECTURER(S)
   (hasanbal@gazi.edu.tr , hhorkcu@gazi.edu.tr)