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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ELK 521OPTIMIZATION TECHNIQUES3 + 01st Semester7,5

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Compulsory
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
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11
LO 00125444      
LO 00225444      
LO 00324555      
LO 00424555      
Sub Total818181818      
Contribution25555000000

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)14570
Mid-terms14040
Final examination14343
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2019-2020 Fall1SERDAR İPLİKÇİ


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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 2019-2020 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Prof. Dr. SERDAR İPLİKÇİ iplikci@pau.edu.tr MUH A0491 %
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