PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 515	NUMERICAL METHODS IN OPTIMISATION	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	The aim of this course is to teach fundamentals necessary to numerically solve complex nonlinear optimization problems.
Course Content	Fundamental concepts and definitions of nonlinear programming, classification, algorithms, convergence, necessary and sufficient conditions and convexity. Unconstrained Optimization methods: First order methods (Newton-Raphson, bisection and golden search). Mathematical foundations of multivariable optimization. Second order methods (Newton, Levenberg-Marquardt). Constrained optimization and Duality.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.

COURSE LEARNING OUTCOMES

1	Lists basic optimization concepts
2	Explains unconstrained optimization
3	Explains constrained optimization
4	Lists optimization algorithms
5	Differentiates between primal and dual forms
6	Karesel programlamayı tanımlar

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001	3	5	2	2		3					2
LO 002	3	5	2	2		3					2
LO 003	3	5	2	2		3					2
LO 004	3	5	2	2		3					2
LO 005	3	5	2	2		3					2
LO 006	3	5	2	2		3					2
Sub Total	18	30	12	12		18					12
Contribution	3	5	2	2	0	3	0	0	0	0	2	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Assignments	5	8	40
Mid-terms	1	15	15
Final examination	1	28	28
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2025-2026 Fall	1	SERDAR İPLİKÇİ

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester	Mode of Delivery
CENG 515	NUMERICAL METHODS IN OPTIMISATION	3 + 0	1	Turkish	2025-2026 Fall	Face to Face

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. SERDAR İPLİKÇİ	iplikci@pau.edu.tr		MUH A0257	%70

Goals		The aim of this course is to teach fundamentals necessary to numerically solve complex nonlinear optimization problems.
Content		Fundamental concepts and definitions of nonlinear programming, classification, algorithms, convergence, necessary and sufficient conditions and convexity. Unconstrained Optimization methods: First order methods (Newton-Raphson, bisection and golden search). Mathematical foundations of multivariable optimization. Second order methods (Newton, Levenberg-Marquardt). Constrained optimization and Duality.

Topics

Weeks	Topics
1	Linear Programming
2	Linear Equations
3	Introduction to Nonlinear Programming
4	Newton-Raphson Method
5	Bisection Method and Golden Search
6	Mathematical Foundations of Multivariable Optimization
7	Conditions for Optimality
8	First Order Methods (Steepest-Descent, Conjugate-Gradient)
9	Second Order Methods (Newton)
10	Second Order Approximate Methods (Levenberg-Marquardt)
11	Genetic Algorithms, Simulated Annealing
12	Lagrange Method
13	Duality
14	Quadratic Programming

Materials

Materials are not specified.

Resources

Resources	Resources Language
Serdar İplikçi Ders Notları	Türkçe

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student