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| IST2007 | Optimization | 4+0+0 | ECTS:5 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Prof. Dr. Türkan ERBAY DALKILIÇ | | Co-Lecturer | PROF. DR. Zafer KÜÇÜK | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To give the solutions of optimization problems, which are confronted in all the basic sciences such as engineering, mathematics, etc. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | form the mathematical model of an optimization problem | 2,4,5 | | | LO - 2 : | solve optimization problems by using siplex algorithm | 2,4,5 | | | LO - 3 : | determine the local and global minimum and maximum points of functions which have reel variable. | 2,4,5 | | | LO - 4 : | solve non-linear programming problems by algorithm acquired in this course | 2,4,5 | | | LO - 5 : | solve optimization problems, which have equality and inequalty constraints | 2,4,5 | | | 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), LO : Learning Outcome | | |
| Structure and types of optimization, classical optimization, general and local maximum and minimum of the function with real variable, non-linear programming problems, optimization with equality restrictions, optimization with inequality restrictions, Kuhn-Tucker theory, optimization methods, algorithm about searching techniques with one-dimension, algorithm about restricted gradient techniques, algorithm about restriction techniques, SUMT algorithm, quadratic programming. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Establishment of mathematical models for optimization problems. | | | Week 2 | The geometric method to solve linear optimization problems. | | | Week 3 | Standardization of linear programming problems, basic solutions. | | | Week 4 | Improving the basic appropriate solution and Primal Simplex method for linear programming. | | | Week 5 | Simplex table. | | | Week 6 | Charnes's M Method. | | | Week 7 | Two-phase method. | | | Week 8 | Duality theory | | | Week 9 | Mid-term exam
| | | Week 10 | Dual simplex method | | | Week 11 | Sensitivity analysis for change in parameters | | | Week 12 | Sensitivity analysis for change in Model structure. | | | Week 13 | Parametric linear programming. | | | Week 14 | Classical optimization. | | | Week 15 | Inequality constrained optimization problems and non-linear programming. | | | Week 16 | End-of-term exam | | | |
| 1 | Apaydın, A., 1996; Optimizasyon, Ankara Üniversitesi Fen Fak. Yayınları, No:41, Ankara | | | |
| 1 | Kara,İ., 2000, Doğrusal Programlama, Bilim Teknik Yayınevi, Ankara | | | 2 | Sucu, M., 1996; Doğrusal Programlama, Bizim Büro Basımevi, Ankara | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 25/11/2021 | 1 | 50 | | End-of-term exam | 16 | 12/01/2022 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 1.5 | 1 | 1.5 | | Dönem sonu sınavı için hazırlık | 17 | 1 | 17 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 128 |
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