Linear Optimization (MDES655) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Linear Optimization MDES655 3 0 0 3 5
Pre-requisite Course(s)
Consent of the instructor
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives This course aims to give to Ph.D. students from different engineering backgrounds the skills of real life problem formulation with linear optimization along with the use of basic computer packages to solve the problems.
Course Learning Outcomes The students who succeeded in this course;
  • 1. Students will have a vision of linear optimization and duality. 2. Students will get a perspective of linear optimization algorithms and be able to code and implement algorithms. 3. Students will develop a vision of the application areas of linear optimization. 4. Students will have a knowledge of decomposition techniques especially for large scaled linear optimization problems. 5. Students will be familiarized with algorithmic complexity and convergence issues.
Course Content Sets of linear equations, linear feasibility and optimization, local and global optima, the Simplex method and its variants, theory of duality and the dual-Simplex method, network-Simplex algorithms, computational complexity issues and interior-point algorithms.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 An introduction to linear feasibility and linear optimization problems. Related pages of the textbook and other courses
2 Geometry of linear optimization, polyhedral sets, extreme points and basic feasible solutions. Related pages of the textbook and other courses
3 The Simplex Algorithm. Related pages of the textbook and other courses
4 Duality theory and complementary slackness. Related pages of the textbook and other courses
5 Sensitivity analysis and parametric linear programming. Related pages of the textbook and other courses
6 The Dual Simplex Algorithm. Related pages of the textbook and other courses
7 Extensions of the Simplex Method. Simplex with upper and lower bounds. Related pages of the textbook and other courses
8 Midterm -
9 Algorithms with sparse matrices and decomposition techniques. Related pages of the textbook and other courses
10 The network-flow problems and the Network Simplex Method. Related pages of the textbook and other courses
11 Application issues of linear optimization. Related pages of the textbook and other courses
12 Algorithmic complexity of the Simplex Method. Related pages of the textbook and other courses
13 The ellipsoid method and an overview of interior-point algorithms. Related pages of the textbook and other courses
14 Algorithm coding and presentations. Related pages of the textbook and other courses
15 Overall review -
16 Final exam -

Sources

Course Book 1. [1] S.G. Nash and A. Sofer, Linear and Nonlinear Programming, McGraw Hill 1996.
Other Sources 2. [2] V. Chvatal, Linear Programming, Freeman 1983.
3. [3] G.L. Nemhauser and L.A. Wolsey, Integer and Combinatorial Optimization, Wiley 1988.
4. [4] H.P. Williams, Model Building in Mathematical Programming, 2nd edition, Wiley, 1985.
5. [5] F.S. Hillier and G.J. Lieberman, Introduction to Mathematical Programming, 2nd edition, McGraw-Hill, 1995.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work 1 15
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 25
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 30
Toplam 6 100
Percentage of Semester Work 70
Percentage of Final Work 30
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems.
2 Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.
4 Graduating researchers active on innovation and entrepreneurship.
5 Ability to report and present research results effectively.
6 Increasing the performance on accessing information resources and on following recent developments in science and technology.
7 An understanding of professional and ethical responsibility.
8 Increasing the performance on effective communications in both Turkish and English.
9 Increasing the performance on project management.
10 Ability to work successfully at project teams in interdisciplinary fields.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship 1 20 20
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 3 6 18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 8 8
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 136