GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
LINEAR OPTIMIZATION/5071307
Course Title: LINEAR OPTIMIZATION
Credits 3 ECTS 7.5
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
He /she knows application areas of optimization
He/she knows the generic structures of general problems linar programming
He /she knows how to develop solutions methods of linear optimization and its algorithmic property
He/she knows the specific importance of the simplex algorithm and its place in linear optimization
He/she knows the meaning and applications of primal and dual models and algorithms in linear optimization
He / she knows about the Kuhn-Tucker optimality conditions in linear optimization
He/she knows about the decomposition methods for large scale linear structures.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  Optimization: Linear optimization, mathematical basis, modeling and examples.
2. Week  Vector space, matrices, system of simultaneous linear equations.
3. Week  Convex sets and convex functions, polyhedral sets.
4. Week  Simplex method: extreme points and optimality, basdic feasible soltions.
5. Week  Simplex method: a key to simplex method, geometric motivation, and its algebra.
6. Week  Starting solution and termination: basic feasible solutions.
7. Week  Starting solution and termination: artificial starting solutions.
8. Week  Starting solution and termination: special cases.
9. Week  Midterm I.
10. Week  Special simplex implementations.
11. Week  Optimality condition on linear programming
12. Week  Duality: formulations and primal-dual relationships.
13. Week  Post-optimality analysis: dual-simplex method
14. Week  Post-optimality analysis: parametrical analysis.
15. Week  Decomposition principle.
16. Week  Final exam
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
40
 Assignment
1
20
 Application
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
60
 Percentage of Final Exam to Total Score  
40
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
15
3
45
 Weekly Tutorial Hours
0
0
0
 Reading Tasks
10
1
10
 Searching in Internet and Library
3
3
9
 Material Design and Implementation
5
1
5
 Report Preparing
2
6
12
 Preparing a Presentation
1
3
3
 Presentation
2
6
12
 Midterm Exam and Preperation for Midterm Exam
2
6
12
 Final Exam and Preperation for Final Exam
1
2
2
 Other (should be emphasized)
5
15
75
 TOTAL WORKLOAD: 
185
 TOTAL WORKLOAD / 25: 
7.4
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1X
2X
3X
4X
5X
6X
7X
8X
9
10X
11X
12X
 -- NAME OF LECTURER(S)
   (Assoc.Prof. Mehmet ATAK and other relevant faculty members)
 -- WEB SITE(S) OF LECTURER(S)
   (www.gazi.edu.tr/~matak)
 -- EMAIL(S) OF LECTURER(S)
   (matak@gazi.edu.tr)