M.SC. in Computer Engineering (Without Thesis)

IE 534 | Course Introduction and Application Information

Course Name

Nonlinear Programming

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 534	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	-
Assistant(s)	-

Course Objectives

The aim of this course is to develop knowledge of different theoretical aspects of nonlinear programming and convex optimization and to give graduate and PhD students the theoretical background on convex analysis and on the theory of optimality conditions, and to provide them with a foundation sufficient to use basic optimization in their own research work and/or to pursue more specialized studies involving optimization theory.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Will be able to interpret convex sets and convex functions
2	Will be able to analyze extreme points and extreme directions of convex sets
3	Will be able to analyze some topological properties of convex sets and convex functions
4	Will be able to use the concept of convexity in the analysis of nonlinear programming problems
5	Will be able to interpret optimality conditions for nonlinear programming problems

Course Description

The course emphasizes the unifying themes such that convex sets and convex functions, their topological properties, separation theorems and optimality conditions for convex optimization problems.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Convex Analysis review and basics
2	Mathematical Preliminaries
3	Mathematical Preliminaries
4	Nonlinear Optimization: Line searches
5	Nonlinear Optimization: Line searches
6	Unconstrained Problems
7	Unconstrained Problems
8	Midterm
9	Constrained Problems
10	Constrained Problems
11	Linearly Constrained Problems
12	Lagrangian Duality
13	Paper Presentations
14	Review and Project Presentations
15	-
16	Final

Course Notes/Textbooks	Nonlinear Programming. Theory and Algorithms., Mokhtar S. Bazaraa, Hanif D. Sherali, C.M. Shetty, John Wiley & Sons, ISBN 0471557935.
Suggested Readings/Materials	Bertsekas, D. Nonlinear Programming, Second Edition, Athena Scientific Publishing, 1999.

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	1	10
Portfolio
Homework / Assignments	1	10
Presentation / Jury
Project	1	25
Seminar / Workshop
Oral Exams
Midterm	1	25
Final Exam	1	30
Total

Weighting of Semester Activities on the Final Grade	4	70
Weighting of End-of-Semester Activities on the Final Grade	1	30
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	8	112
Field Work			0
Quizzes / Studio Critiques	1		0
Portfolio			0
Homework / Assignments	4	6	24
Presentation / Jury		6	0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	10	10
Final Exam	1	15	15
		Total	209

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	-	X
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	X	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	-	X
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	X	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	X	-	-	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	X	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-