Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST422 OPTIMIZATION MODELS AND APPLICATIONS Elective 4 8 5
Level of Course Unit
First Cycle
Objectives of the Course
The aim of this course is to provide students with the ability to model different types of optimization problems and to be able to code current methods that solve such problems.
Name of Lecturer(s)
Doç. Dr. Ali MERT
Learning Outcomes
 1 To be able to comprehend the theoretical background of optimization. 2 To be able to recognize different types of optimization problems. 3 To be able to model an optimization problem of the first time. 4 Choose the method that can solve any optimization problem 5 To be able to apply any method that solves an optimization problem. 6 To be able to follow current publications about optimization. 7 To be able to create the necessary codes to solve optimization problems.
Mode of Delivery
Face to Face
Prerequisites and co-requisities
No
Recommended Optional Programme Components
No
Course Contents
General information about the concept of optimization. Basic concepts of constrained nonlinear optimization problems. Methods for solving constrained nonlinear optimization problems. Different optimization problems and methods to solve them.
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 1 Mathematical foundations of optimization. Structure of optimization problem. Guided Problem Solving 2 Basic theorems about constrained nonlinear optimization problems. Guided Problem Solving 3 Zoutendijk method and applications. Guided Problem Solving 4 Rosen method and applications. Guided Problem Solving 5 Linear combinations method. Guided Problem Solving 6 Fundamentals of quadratic programming. Guided Problem Solving 7 Fundamentals of stochastic programming. Guided Problem Solving 8 Midterm 9 Basic information about C ++. Guided Problem Solving 10 Coding of the Divide method. Guided Problem Solving 11 Coding of the Golden Ratio method. Guided Problem Solving 12 Coding of Fibonacci method. Guided Problem Solving 13 Coding of Newton method. Guided Problem Solving 14 Encoding the nearest neighborhood algorithm Guided Problem Solving 15 Coding of Artificial Bee Colony algorithm Guided Problem Solving 16 Final exam
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight SUM 0 End Of Term (or Year) Learning Activities Quantity Weight SUM 0 SUM 0
Language of Instruction
Turkish
Work Placement(s)
No