PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
EEEN 479	OPTIMIZATION TECHNIQUES	3 + 0	7th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	To teach gradient-based unconstrained numerical optimization techniques. To implement these techniques in MatLab environment. To use 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

1	He/She knows the fundamental concepts of numerical optimization.
2	He/She knows gradient-based unconstrained numerical optimization methods.
3	He/She can solve related real-world problems by optimization methods.
4	He/She can do modeling and prediction by Artificial Neural Networks.

COURSE'S CONTRIBUTION TO PROGRAM

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
Mid-terms	1	8	8
Final examination	1	10	10
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
EEEN 479	OPTIMIZATION TECHNIQUES	3 + 0	3	Turkish	2015-2016 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Assoc. Prof. Dr. MERİÇ ÇETİN	mcetin@pau.edu.tr		MUH A02151	%

Goals		To teach gradient-based unconstrained numerical optimization techniques. To implement these techniques in MatLab environment. To use 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

Weeks	Topics
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

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

Prospective Student