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| BILB2005 | Optimization | 4+0+0 | ECTS:5 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | COMPUTER SCIENCE | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Prof. Dr. Türkan ERBAY DALKILIÇ | | Co-Lecturer | Prof. Dr. Türkan ERBAY DALKILIÇ | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | By teaching optimization techniques, to equip students on how to solve and interpret optimization problems that may be encountered in almost every basic science branch in the public and private sectors, from business to engineering, from mathematics to science. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Can model an optimization problem mathematically. | 2,4,5 | 1, | | LO - 2 : | Can solve mathematically modeled problems using the simplex algorithm. | 2,4,5 | 1, | | LO - 3 : | Can solve nonlinear programming problems with the taught algorithms. | 2,4,5 | 1, | | LO - 4 : | Can solve problems with equality and inequality constraints using optimization methods. | 2,4,5 | 1, | | 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, mathematical modeling of optimization problems, solution of classical optimization problems, nonlinear programming problems, optimization problems with equality constraints, optimization problems with inequality constraints, dual simplex method, Kuhn-Tucker conditions, post-optimality analysis, parametric programming and quadratic programming. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Establishing mathematical models of optimization problems. | | | Week 2 | Solving linear optimization problems using the geometric method. | | | Week 3 | Standardization of linear programming problem and basic solutions. | | | Week 4 | Primal Simplex method for basic feasible solution optimization and linear programming. | | | Week 5 | Simplex table. | | | Week 6 | Charnes' M method. | | | Week 7 | Two-phase method. | | | Week 8 | duality theory | | | Week 9 | Mid-term exam | | | Week 10 | | | | Week 11 | Post-best analysis for changes in parameters. | | | Week 12 | Post-optimality analysis for changes in model structure. | | | Week 13 | Parametric linear programming. | | | Week 14 | Classic optimization. | | | Week 15 | Inequality constrained optimization problems and nonlinear 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 | | 1 | 50 | | End-of-term exam | 16 | | 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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