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| YZM3034 | Optimization Theory | 2+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Emin TUĞCU | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To gain the ability to use basic mathematical and physics principles in optimum design of engineering systems, to make a logical and systematic approach for optimum solution of engineering problems. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Knows the basic concepts of optimization | 1,2,3,4 | 1, | | LO - 2 : | Knows derivative-based unconstrained numerical optimization methods | 1,2,3,4 | 1, | | LO - 3 : | Can solve real world problems with optimization methods | 1,2,3,4 | 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 | | |
| What is optimization, What are its applications in engineering, Definition and classification of optimization problem, Graphical optimization, Classical optimization techniques
Univariate optimization , Unconstrained multivariate optimization, Equation constrained multivariate optimization , Nonlinear programming, One dimensional unconstrained optimization
Geometric programming, Unconstrained geometric programming problems, Geometric programming, Constrained geometric programming problems, Linear programming |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Optimization, What is Optimization. What are its applications in engineering | | | Week 2 | Definition and classification of the optimization problem | | | Week 3 | Graphical optimization | | | Week 4 | Classical Optimization Methods: Univariate Optimization | | | Week 5 | Bivariate Optimization | | | Week 6 | Unconstrained Multivariate Optimization: Matrices, Quadratic Format | | | Week 7 | Unconstrained Multivariate Optimization, Gradient Vector, Hessien Matrix | | | Week 8 | Equality Constrained Multivariate Optimization: Direct Substitution Method | | | Week 9 | Midterm | | | Week 10 | Equality Constrained Multivariate Optimization: Lagrange Multipliers Method | | | Week 11 | Inequality Constrained Multivariate Optimization: Kuhn-Tucker Conditions | | | Week 12 | Non-Linear Programming (Single Variable Optimization Methods): Unconstrained Search, Exact Search, Constant Search | | | Week 13 | Two-Symmetric Point Search, Three-Division Search | | | Week 14 | Gradient Methods: Fastest Ascent Method, Fastest Descend Method | | | Week 15 | Linear programming | | | Week 16 | End-of-term exam | | | |
| 1 | Engineering Optimization and Application, ,Singiresu S. Rao, , ,Wiley Eastern Limited,,0-4714555034-5,Kanada,1996 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 50 | | 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 | 2 | 14 | 28 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 2 | 1 | 2 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 2 | 15 | 30 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 120 |
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