Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
STA 5012	NONLINEAR OPTIMIZATION	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR EFENDI NASIBOĞLU

Offered to

Computer Science
Statistics
Statistics
Ph.D. in Computer Science
STATISTICS

Course Objective

The objective of this course is to introduce basics of theory, methods and algorithms of nonlinear optimization (NO).

Learning Outcomes of the Course Unit

1	Have a good understanding of basic NO theory
2	Have a good understanding about the basic NO algorithms.
3	Have a basic knowledge of heuristic NO algorithms.
4	Have ability to use basic NO software.
5	Have ability to design NO problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	INTRODUCTION
2	CONVEX SETS
3	CONVEX FUNCTIONS
4	THE OPTIMALITY CONDITIONS, ASSIGMENT1
5	LAGRANGIAN DUALITY
6	LINE SEARCH ALGORITHMS, ASSIGMENT2
7	MULTIDIMENSIONAL ALGORITHMS
8	MIDTERM EXAM
9	PENALTY FUNCTIONS
10	BARRIER FUNCTIONS
11	METHODS OF FEASIBLE DIRECTIONS
12	SOME SPECIAL PROGRAMMING PROBLEMS, ASSIGMENT3
13	NONLINEAR OPTIMIZATION APPLICATIONS
14	NONLINEAR OPTIMIZATION APPLICATIONS (continued)

Recomended or Required Reading

Mokhtar S. Bazaraa, C.M.Shetty, Nonlinear Programming, Theory and Algorithms,byWiley-Interscience, 1979
Supplementary Book(s): Stephen Boyd, Lieven Vanderberghe, Convex Optimization, by Cambridge University Press, 2004.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	ASG	ASSIGNMENT
2	MTE	MIDTERM EXAM
3	FIN	FINAL EXAM
4	FCG	FINAL COURSE GRADE	ASG * 0.30 + MTE * 0.30 + FIN * 0.40
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	ASG * 0.30 + MTE * 0.30 + RST * 0.40

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

efendi.nasibov@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	13	4	52
Preparation before/after weekly lectures	13	4	52
Preparation for Midterm Exam	1	20	20
Preparation for Final Exam	1	30	30
Preparing Individual Assignments	3	15	45
Final	1	2	2
Midterm	1	2	2
TOTAL WORKLOAD (hours)			203

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.4	PO.5	PO.6
LO.1	5	5	5	5
LO.2	5	5	5	5
LO.3	5	5	5	5
LO.4	5	5	5	5
LO.5	5	5	5	5

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION