Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
213300004129OPTIMIZATION APPLICATIONS WITH R Elective245
Level of Course Unit
Short Cycle
Objectives of the Course
The aim of this course is to equip students with the ability to analyze and solve various optimization problems using the R programming language. Students will receive a general introduction to the fundamental concepts and techniques of optimization and will learn to apply these concepts practically using the tools provided by the R language.
Name of Lecturer(s)
Öğr. Gör. Dr. Hakan DUMAN
Learning Outcomes
1Recognition and Formulation of Optimization Problems
2Utilization of the R Programming Language
3Solution of Linear and Nonlinear Optimization Problems:
4Application of Decision Trees and Dynamic Programming
5Application of Simulation-Based Optimization Techniques
Mode of Delivery
Daytime Class
Prerequisites and co-requisities
Recommended Optional Programme Components
Course Contents
Fundamental Concepts of Optimization: Definition of optimization problems, identification of constraints, and determination of objective functions. Optimization of Univariate and Multivariate Functions: Finding maximum and minimum points of univariate functions, and utilizing gradient and Hessian matrices to find maximum and minimum points of multivariate functions. Linear Programming (LP) Problems: Identification, solution, and implementation of linear programming problems using R. Integer Programming (IP) Problems: Definition of integer programming problems, solution techniques, and practical applications using R. Decision Trees and Dynamic Programming: Concepts of decision trees and dynamic programming, their application to optimization problems, and practical applications using R. Discrete and Mixed Integer Programming: Definition of discrete and mixed integer programming problems, solution techniques, and practical applications using R. Simulation-Based Optimization: Monte Carlo methods, genetic algorithms, and simulation-based optimization techniques, along with practical applications using R.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Utilization of the R Programming Language: Students will learn the basic structures and functions of the R programming language, enabling them to effectively solve optimization problems using R.Utilization of the R Programming Language: Students will learn the basic structures and functions of the R programming language, enabling them to effectively solve optimization problems using R.
2Utilization of the R Programming Language: Students will learn the basic structures and functions of the R programming language, enabling them to effectively solve optimization problems using R.Utilization of the R Programming Language: Students will learn the basic structures and functions of the R programming language, enabling them to effectively solve optimization problems using R.
3Utilization of the R Programming Language: Students will learn the basic structures and functions of the R programming language, enabling them to effectively solve optimization problems using R.Utilization of the R Programming Language: Students will learn the basic structures and functions of the R programming language, enabling them to effectively solve optimization problems using R.
4Fundamental Concepts of Optimization: Definition of optimization problems, identification of constraints, and determination of objective functions.
5Optimization of Univariate and Multivariate Functions: Finding maximum and minimum points of univariate functions, and utilizing gradient and Hessian matrices to find maximum and minimum points of multivariate functions.
6Linear Programming (LP) Problems: Identification, solution, and implementation of linear programming problems using R.
7Integer Programming (IP) Problems: Definition of integer programming problems, solution techniques, and practical applications using R.
8Decision Trees and Dynamic Programming: Concepts of decision trees and dynamic programming, their application to optimization problems, and practical applications using R.
9Discrete and Mixed Integer Programming: Definition of discrete and mixed integer programming problems, solution techniques, and practical applications using R.
10Simulation-Based Optimization: Monte Carlo methods, genetic algorithms, and simulation-based optimization techniques, along with practical applications using R.
11Solve real-world optimization problems
12Solve real-world optimization problems
13Solve real-world optimization problems
14Solve real-world optimization problems
Recommended or Required Reading
Primary Sources: Theoretical Foundation: Pehlivanoğlu, Y. V., 2017, Optimizasyon: Temel Kavramlar & Yöntemler, 1st Edition, Ankara R Applications: Şirin, SM, 2018, R ile UYGULAMALI ANALİZ YÖNTEMLERİ I : İstatistiğe Giriş ve Açıklayıcı Veri Analizi Supplementary Sources: Mykel J. Kochenderfer and Tim A. Wheeler, MIT Press, 2019 (https://algorithmsbook.com/optimization/files/optimization.pdf) Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, MIT Press, 2022 (https://algorithmsbook.com/files/dm.pdf)"
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
Practice1258
Homework542
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Project Presentation1100
SUM100
Term (or Year) Learning Activities70
End Of Term (or Year) Learning Activities30
SUM100
Language of Instruction
Turkish
Work Placement(s)
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Practice12336
Project Preparation14040
Homework51575
TOTAL WORKLOAD (hours)151
Contribution of Learning Outcomes to Programme Outcomes
PO
1		PO
2		PO
3		PO
4		PO
5		PO
6		PO
7		PO
8		PO
9		PO
10
LO1         5
LO2         5
LO3         5
LO4         5
LO5         5
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Iğdır University, Iğdır / TURKEY • Tel (pbx): +90 476 226 13 14 • e-mail: info@igdir.edu.tr