ACADEMICS
Course Detail

ELE 704 Optimization
2016-2017 Spring term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://ects.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.

ELE704 - OPTIMIZATION

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
OPTIMIZATION ELE704 Any Semester/Year 3 0 3 10
Prerequisite(s)None
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
 
Instructor (s)Assoc.Prof.Dr. Cenk Toker, Asst.Prof.Dr. Umut Sezen 
Course objectiveIt is aimed to give the following topics to the students; a) Recognising and classifying an optimisation problem, b) Tools for learning and analysing convex sets and functions, c) Basic algorithms used in solving convex optimisation problems, d) Duality concept in constrained problems and the techniques being used to apply them, mainly staying in the context of convex optimisation, so that they can solve problems which they may encounter with in their studies/projects.  
Learning outcomes
  1. Recognise and classify optimisation problems
  2. Model the problem s/he encounters with as an optimisation problem
  3. Know which algorithms can s/he use to solve the problem s/he established, know the advantages and disadvantages of these algorithms
  4. Apply the techniques and algorithms s/he learnt in the class to her/his thesis studies and also real-life applications
  5. Have the adequate knowledge to follow and understand advanced up-to-date optimisation algorithms
Course ContentBrief reminder of linear algebra topics,
Convexity, convex sets and functions,
Gradiant Descent, Steepest Descent, Newton Algorithms and their variations for unconstrained problems,
Constrained problems and Karush-Kuhn-Tucker Conditions,
Modification of the above algorithms for unconstrained problems to constrained problems,
Ưnterior Point Algorithms (Penalty ve Barrier Methods)
 
References1. Luenberger, Linear and Nonlinear Programming, Kluwer, 2002.
2. Boyd and Vandenberghe, Convex Optimization, Cambridge, 2004.
3. Baldick, Applied Optimization, Cambridge, 2006.
4. Freund, Lecture Notes, MIT.
5. Bertsekas, Lecture Notes, MIT.
6. Bertsekas, Nonlinear Programming, Athena Scientific, 1999.
 

Course outline weekly

WeeksTopics
Week 1Brief reminder of linear algebra topics
Week 2Brief reminder of linear algebra topics
Week 3Optimality conditions for unconstrained problems Convex Sets
Week 4Convex and concave functions Conditions for convexity Operations that preserve convexity
Week 5Quadratic functions, forms and optimization Optimality conditions Unconstrained minimization
Week 6Descent Methods Convergence
Week 7Algorithms: Gradient Descent Algorithm
Week 8Algorithms: Steepest Descent Algorithm
Week 9Algorithms: Newton?s Algorithm
Week 10Midterm Exam
Week 11Constrained optimization Duality
Week 12Optimality conditions, KKT Conditions Algorithms: Feasible Direction Method, Active Set Method
Week 13Algorithms: Gradient Projection Method, Newton?s Algorithm with Equality Constraints
Week 14Algorithms: Penalty and Barrier Methods
Week 15Study week
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments1330
Presentation00
Project00
Seminar00
Midterms130
Final exam140
Total100
Percentage of semester activities contributing grade succes1460
Percentage of final exam contributing grade succes140
Total100

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)149126
Presentation / Seminar Preparation000
Project000
Homework assignment13565
Midterms (Study duration)13131
Final Exam (Study duration) 13636
Total Workload4384300

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering.   X 
2. Has knowledge, skills and and competence to develop novel approaches in science and technology.   X 
3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research.  X  
4. Can independently carry out all stages of a novel research project. X   
5. Designs, plans and manages novel research projects; can lead multidisiplinary projects.X    
6. Contributes to the science and technology literature.   X 
7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English. X   
8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them.X    

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Academic Calendar | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Information for ELE 401-402 Graduation Project | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering