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| BIL3004 | Optimization | 3+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to teach when to use the optimization techniques in the computer applications. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | describe the optimization problems in mathematical form. | 2,3,4,12 | 1,3 | | LO - 2 : | define optimal solutions on single and multi-variable searching techniques. | 2,3,4,12 | 1 | | LO - 3 : | describe basics of operational research and development of its projects. | 2,3,4,12 | 1,3 | | LO - 4 : | apply game theory to computer science and management science. | 2,3,4,12 | 1,3 | | LO - 5 : | define linear and dynamic programming techniques and apply these techniques in computer science and other fields. | 2,3,4,12 | 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), LO : Learning Outcome | | |
| Linear Inequality System, Linear Programming, Simplex Method, Nonlinear Programming, Main Solutions, Infinite Solutions, Artificial Beginning Solution, Finding Local Extremum in Nonlinear Programming, Convex and Concave Functions, Multidimensional Optimization Problems, Lagrange Method, Jacobi Method, Numerical Optimization Methods, Approximation Methods, Steep Descent Method, Conjugate Direction Method, Hill Climbing Method. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Optimization. Basic Conseps and Terminoplogy | | | Week 2 | Single-variable optimization | | | Week 3 | Fibonacci ve Altın Oran Yöntemleri | | | Week 4 | Multi-variable Optimization | | | Week 5 | Pattern Search. Hook and Jeeves method | | | Week 6 | Gradient. Quadratic and Hessian matrixs | | | Week 7 | Linear Programming | | | Week 8 | Mid-term exam | | | Week 9 | Simplex Method. Two phase method. | | | Week 10 | Game theory. Payoff matrix | | | Week 11 | Zero-sum and non-zero-sum problems. | | | Week 12 | Short Exam | | | Week 13 | Dynamic Programmin. LCS problem | | | Week 14 | Edit Distance Method. Levenstein Algorithm | | | Week 15 | 0-1 Knapsack Problem | | | Week 16 | End-of-term exam | | | |
| 1 | Adby P. R. and Dempster M. A. H. , 1985, Introduction to Optimization Methods. London. | | | 2 | Elster K. H. , 1993, Modern Mathematical Methods of Optimization, Vch Pub. ISBN 3055014529 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 2 | 30 | | Quiz | 12 | | 2 | 20 | | End-of-term exam | 16 | | 2 | 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 | 3 | 14 | 42 | | Laboratuar çalışması | 4 | 7 | 28 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 2 | 1 | 2 | | Kısa sınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 14 | 1 | 14 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 100 |
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