EEE5010 OptimizationBahçeşehir UniversityDegree Programs MECHATRONICS ENGINEERING (ENGLISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5010 Optimization Fall 3 0 3 9
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. SÜREYYA AKYÜZ
Course Lecturer(s): Prof. Dr. SÜREYYA AKYÜZ
Recommended Optional Program Components: None
Course Objectives: To equip students with the mathematical theory of optimization and solution methods.

Learning Outcomes

The students who have succeeded in this course;
Students will
- be able to formulate optimization problems
- understand basic differences between various constraints
- apply numerical techniques to solve optimization problems

Course Content

Optimization as a decision making problem. Optimization over an open set. Optimization under equality constraints; Lagrange multipliers. Optimization under inequality constraints. Linear programming. Numerical methods.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) The Optimization Problem. Examples.
2) Mathematical preliminaries
3) Mathematical preliminaries
4) The Weierstrass Theorem. Application to example problems.
5) Optimization over an open set. Necessary and sufficient conditions.
6) Numerical techniques: Gradient algorithm, Newton's method.
8) Optimization with equality constraints. Lagrange multipliers.
9) Optimization with inequality constraints. Kuhn-Tucker conditions.
10) Linear programming: Standard maximization and minimization problems.
11) Linear programming: Primal and dual problems. Duality theorem. Optimality conditions.
12) The Simplex algorithm.
13) Discrete dynamic programming.
14) Large optimization problems. Decomposition methods.


Course Notes / Textbooks: 1. P. Varaia, Lecture Notes on Optimization, web
References: 1. C.T. Kelley, Iterative Methods for Optimization, SIAM

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 5 % 25
Midterms 1 % 25
Final 1 % 50
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 16 136
Homework Assignments 5 10
Midterms 1 2
Final 1 2
Total Workload 192

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution