1. FIRST CYCLE - BACHELOR'S DEGREE
  2. FACULTY OF ENGINEERING
  3. COMPUTER ENGINEERING DEPARTMENT
  4. 253 Computer Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 314NUMERIC OPTIMIZATION3 + 06th Semester4

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Compulsory
Course Objective The aim of this course is to teach gradient-based unconstrained optimization methods and to implement these methods in popular software environments.
Course Content One dimensional nonlinear numerical optimization / Multi-dimensional nonlinear numerical optimization / Mathematical background / Analytical conditions for optimality / First-order methods / Second-order Methods / Second-order approximate methods / Modeling and prediction / Linear and nonlinear models / Numerical optimization applications with popular software environments
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Understands the fundamental concepts of numerical optimization.
2Understands one dimensional and multi-dimensional unconstrained numerical optimization methods.
3Solves related real-world problems by optimization methods.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001 443        
LO 002 555        
LO 0034544        
Sub Total4141312        
Contribution154400000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14228
Mid-terms11212
Final examination12222
Total Work Load

ECTS Credit of the Course





104

4
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Spring1SERDAR İPLİKÇİ
Details 2022-2023 Spring1SERDAR İPLİKÇİ


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
CENG 314 NUMERIC OPTIMIZATION 3 + 0 1 Turkish 2023-2024 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Prof. Dr. SERDAR İPLİKÇİ iplikci@pau.edu.tr SABF C0206 %70
Goals The aim of this course is to teach gradient-based unconstrained optimization methods and to implement these methods in popular software environments.
Content One dimensional nonlinear numerical optimization / Multi-dimensional nonlinear numerical optimization / Mathematical background / Analytical conditions for optimality / First-order methods / Second-order Methods / Second-order approximate methods / Modeling and prediction / Linear and nonlinear models / Numerical optimization applications with popular software environments
Topics
WeeksTopics
1 Introduction to optimization, optimization problem
2 Unconstrained optimization
3 Numeric Optimization
4 Indirect Methods: Newton Raphson, Bisection methods and Matlab applications
5 Direct Methods: Golden Section and Matlab applications
6 Algorithms for unconstrained multivariable optimization
7 Gradient methods
8 Steepest Descent, Conjugate Gradient methods
9 Midterm
10 Newton methods, Quasi-Newton methods
11 Non-Gradient Methods, Regression
12 Linear and Nonlinear Models
13 SISO Neural Network Model and Matlab applications
14 MIMO Neural Network Model and Matlab applications
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes