COURSE UNIT TITLE

: LINEAR AND NONLINEAR PROGRAMMING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5090 LINEAR AND NONLINEAR PROGRAMMING 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 MUSTAFA ALPER SELVER

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM) (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

Students are able to learn widely used optimization techniques in all fields of engineering, the motivation behind the use of these techniques and the algorithms to determine the "best" or "most desirable" solution to a problem.

Learning Outcomes of the Course Unit

1   The students are expected to learn what linear programming is and algorithmic approaches for linear programming such as the simplex method.
2   The students are expected to understand the special cases of linear programming such as network and transportation problems.
3   The students are expected to solve real world linear programming problems by hand and/or writing programmes
4   The students are expected to gain basic skills about the fundamentals of non-linear pogramming and its most basic algorithms.
5   The students are expected to prepare a technical report related with the term project

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Optimization, Types and Sizes of Problems, Iterative Algorithms and Convergence
2 Examples of Linear Programming Problems and Basic Solutions, The Fundamental Theorem of Linear Programming. Exercises and Programming Environments
3 The Simplex Method. Pivots and Adjacent Extreme Points. Determining a Minimum Feasible Solution. Computational Procedure
4 Duality. Dual Linear Programs. The Duality Theorem. Relations to the Simplex Procedure
5 Transportation and Network Flow Problems, The Transportation Problem, Basic Network Concepts, Minimum and Maximum Flow
6 Midterm Exam I
7 Unconstrained Problemsi. Examples of Unconstrained Problems. Convex and Concave Functions. Minimization and Maximization of Convex Functions. Global Convergence and Speed of Convergence
8 Descent Methods. Line Search by Curve Fitting. The Method of Steepest Descent. Newton s Method
9 Conjugate Direction Methods. Conjugate Directions. The Conjugate Gradient Method
10 Quasi-Newton Methods. Modified Newton Method. Conjugate Directions. The Conjugate Gradient Method
11 Midterm Exam II
12 Constrained Minimization. Constrained Minimization Conditions
13 Primal Methods. Feasible Direction Methods. The Gradient Projection Method. The Reduced Gradient Method
14 Penalty and Barrier Methods

Recomended or Required Reading

Textbook(s): David G. Luenberger, Yinyu Ye, Linear and Nonlinear Programming, 3rd edition, Springer, NY, 2008.

Supplementary Book(s):
Dimitri P. Bertsekas, Nonlinear Programming, 2nd edition, Athena Scientific, NJ, 1999.

Stephen G. Nash, Ariela Sofer, Linear and Nonlinear Programming, 2nd edition, McGraw-Hill, NY, 1995

Planned Learning Activities and Teaching Methods

Lectures, Homeworks, Term project

Assessment Methods

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

Exam

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

alper.selver@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparation for Mid-term Exam 2 6 12
Preparing Individual Assignments 12 3 36
Preparing Term Project/Presentations 1 40 40
Preparation before/after weekly lectures 12 3 36
Preparation for Final Exam 1 20 20
Final 1 3 3
Midterm 2 3 6
TOTAL WORKLOAD (hours) 189

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.1544352122214211
LO.2544453231225211
LO.3545554533345422
LO.4544352222224211
LO.5344124115422111

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