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| IST5032 | Optimization Techniques | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | Assoc. Prof. Dr. Zafer Küçük | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To enable students to understand modeling of optimization problems, analysis and interpretation of the optimization problem. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | determine whether the function is convex | 2,5,7,8 | 1,3 | | PO - 2 : | set up mathematical models of optimization problems | 1,2,3,5 | 1,3 | | PO - 3 : | determine the appropriate method for the solution of optimization problems | 1,2,3,5 | 1,3 | | PO - 4 : | obtaine the solution for optimization problems, by applying the determined method. | 1,2,3,4,5 | 1,3 | | PO - 5 : | interpret the results from the solution. | 1,2,3,4,5,6 | 1,3 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Convex, concave function, single variable optimization, multivariable optimization with and without constraints, Lagrange methods, Kuhn-Tucher theory, convex analysis, linear and nonlinear programming, quadratic programming, genetic algorithms and applications, stochastic programming. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic definitions and concepts. | | | Week 2 | Concavity, convexity. | | | Week 3 | Optimization of unrestricted single-variable functions. | | | Week 4 | Optimization of unrestricted -variable functions | | | Week 5 | Equality constrained optimization problems: lagrange multipliers method. | | | Week 6 | Inequality constrained optimization problems: Kuhn-Tucher conditions. | | | Week 7 | Assumptions for linear programming. | | | Week 8 | Mathematical model of linear programming problems. | | | Week 9 | Mid-term exam
| | | Week 10 | Solution techniques for linear programming problems. | | | Week 11 | Non-linear programming methods: Gold cut method, Fibonacci method. | | | Week 12 | Quadratic programming. | | | Week 13 | Stochastic programming. | | | Week 14 | Discrete programming. | | | Week 15 | Genetic algorithms and applications. | | | Week 16 | End-of-term exam | | | |
| 1 | Bal, H., 1995, Optimizasyon Teknikleri, Gazi Üniversitesi, Ankara. | | | |
| 1 | Hamdy, T., 2000, Yöneylem araştırması, Literatür yayınları, İstanbul. | | | 2 | Apaydın, A., 1996; Optimizasyon, Ankara Üniversitesi Fen Fak. Yayınları, No:41, Ankara | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 15/11/2016 | 2,0 | 50 | | End-of-term exam | 16 | 06/01/2017 | 2,0 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 2 |
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