Course Details

ELE 604 Optimization
2020-2021 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 ( in real-time and displayed here. Please check the appropriate page on the original site against any technical problems. Course data last updated on 01/03/2021.


Course Name Code Semester Theory
Credit ECTS
OPTIMIZATION ELE604 Any Semester/Year 3 0 3 8
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
Instructor (s)Department Faculty 
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

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
Field activities00
Specific practical training00
Final exam140
Percentage of semester activities contributing grade succes1460
Percentage of final exam contributing grade succes140

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14570
Presentation / Seminar Preparation000
Homework assignment13565
Midterms (Study duration)12929
Final Exam (Study duration) 13434
Total Workload4376240

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.   X 
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.   X 
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.  X  
4. Designs and runs research projects, analyzes and interprets the results.  X  
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.X    
6. Produces novel solutions for problems.  X  
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. X   
8. Follows technological developments, improves him/herself , easily adapts to new conditions.   X  
9. Is aware of ethical, social and environmental impacts of his/her work.X    
10. Can present his/her ideas and works in written and oral form effectively; uses English effectivelyX    

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

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