COURSE UNIT TITLE

: NONLINEAR OPTIMIZATION

DEGREE PROGRAMMES

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/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.135555
LO.255445
LO.353455
LO.454555
LO.545535

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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