1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. BIOMEDICAL ENGINEERING DEPARTMENT
  4. 1507 Biomedical 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
ELK 521OPTIMIZATION TECHNIQUES3 + 01st Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective To teach Fundamentals related to optimization techniques and to gain graduate students the ability of using these techniques in solving real world problems.
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 / Applications
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Knows the fundamental concepts of numerical optimization
2Knows gradient-based unconstrained numerical optimization methods
3Can solve related real-world problems by optimization methods
4Can do modeling and prediction by Artificial Neural Networks

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Mid-terms15656
Final examination15656
Special Study Module (Student)14141
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Fall1EŞREF BOĞAR


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
ELK 521 OPTIMIZATION TECHNIQUES 3 + 0 1 Turkish 2023-2024 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Asts. Prof. Dr. EŞREF BOĞAR ebogar@pau.edu.tr TEK A0302 %70
Goals To teach Fundamentals related to optimization techniques and to gain graduate students the ability of using these techniques in solving real world problems.
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 / Applications
Topics
WeeksTopics
1 One-dimensional nonlinear numerical optimization: Gradient-based methods: Newton-Raphson method, Bisection method.
2 One-dimensional nonlinear numerical optimization: Nongradient-based methods: golden-Section method, importance of one-dimensional nonlinear numerical optimization
3 Multi-dimensional nonlinear numerical optimization: Problem definition, general update rule, mathematical basics.
4 Multi-dimensional nonlinear numerical optimization: Analytical conditions for optimality
5 Multi-dimensional nonlinear numerical optimization: First-order methods: Steepest-Descent, Conjugate-Gradient
6 Multi-dimensional nonlinear numerical optimization: Second-order methods: Newton’s method
7 Multi-dimensional nonlinear numerical optimization: Second-order methods: Modified Newton’s method, Cholesky factorization.
8 Multi-dimensional nonlinear numerical optimization: Quasi-Newton method: Davidon-Fletcher-Powell method, Broydon-Fletcher-Goldfarb-Shanno method
9 MIDTERM EXAM
10 Multi-dimensional nonlinear numerical optimization: Second-order approximate methods: Gauss-Newton method, Levenberg-Marquardt method
11 Applications: Single-Input Single-Output (SISO) Regression problem: polynomial model, RBF model, exponential model
12 Applications: Single-Input Single-Output (SISO) Regression problem: SISO Artificial Neural Network (ANN) model.
13 Applications: Multiple-Input Single-Output (MISO) Regression problem: MISO Artificial Neural Network (ANN) model.
14 Applications: Multiple-Input Single-Output (MISO) Regression problem: Modeling and prediction by MISO Artificial Neural Network (ANN) model.
Materials
Materials are not specified.
Resources
ResourcesResources Language
1- Nonlinear Programming, NASH and SOFER, McGraw-Hill, 1996.English
2- Nonlinear Programming, BERTSEKAS, Athena Scientific, 1999. English
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