ECTS - Introduction to Optimization
Introduction to Optimization (MFGE412) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Introduction to Optimization | MFGE412 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Technical Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Question and Answer, Drill and Practice. |
| Course Lecturer(s) |
|
| Course Objectives | Implementation of optimization methods using MATLAB. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Introduction to optimization, graphical optimization, least squares regression, linear and non-linear programming, numerical techniques, unconstrained and constrained optimization, global optimization (genetic algorithm), applications. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to optimization. | Chapters 13 |
| 2 | Numerical Techniques | Chapter 13 |
| 3 | Graphical Optimization | Chapter 15 |
| 4 | Curve fitting (linear) | Chapter 17 |
| 5 | Curve fitting (linearizing) | Chapter 17 |
| 6 | Linear Programming | Chapter 15 |
| 7 | Linear Programming | Chapter 15 |
| 8 | Nonlinear Programming | Chapter 14 |
| 9 | Constrained Optimization | Chapter 15 |
| 10 | Genetic Algorithm | Lecture notes |
| 11 | Genetic Algorithm | Lecture notes |
| 12 | MATLAB Applications | Chapter 15 |
| 13 | Applications on Engineering Problems | Chapter 16 and private study |
| 14 | Applications on Engineering Problems | Chapter 16 and private study |
| 15 | Final exam period | All chapters |
| 16 | Final exam period | All chapters |
Sources
| Course Book | 1. Numerical Methods for Engineers, S.C. Chapra & R.P. Canale, 5th Edition, McGraw-Hill, 2006 |
|---|---|
| Other Sources | 2. Applied Optimization with MATLAB Programming, Wiley, by P. Venkataraman (2002). |
| 3. Practical Optimization (Algorithms and Engineering Applications), (Springer) by Antoniou, Andreas and Lu, Wu-Sheng (2007). | |
| 4. Numerical Optimization (Springer) by Jorge Nocedal and Stephen Wright (2006) | |
| 5. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-based Algorithms (Applied Optimization) by Jan A. Snyman (2005) |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 1 | 5 |
| Laboratory | 5 | 25 |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 40 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 9 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | |
|---|---|
| Major Area Courses | X |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Knowledge of mathematics, natural sciences, engineering fundamentals, computing, and topics specific to the relevant engineering discipline; the ability to use this knowledge in the solution of complex engineering problems. | X | ||||
| 2 | The ability to identify, formulate, and analyze complex engineering problems using knowledge of basic sciences, mathematics, and engineering, and considering the UN Sustainable Development Goals relevant to the problem. | X | ||||
| 3 | The ability to design creative solutions for complex engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, considering realistic constraints and conditions. | X | ||||
| 4 | The ability to select and use appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling, for the analysis and solution of complex engineering problems, with an awareness of their limitations. | X | ||||
| 5 | The ability to use research methods for the investigation of complex engineering problems, including literature search, designing and conducting experiments, collecting data, and analyzing and interpreting results. | |||||
| 6 | Knowledge of the effects of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions. | X | ||||
| 7 | Acting in accordance with engineering professional principles, knowledge of ethical responsibility; awareness of acting impartially without discrimination on any grounds and being inclusive of diversity. | X | ||||
| 8 | The ability to work effectively individually and in intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid) as a team member or leader. | |||||
| 9 | "The ability to communicate effectively orally and in writing on technical topics, considering the various differences of the target audience (such as education, language, profession). | |||||
| 10 | Knowledge of practices in business life such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation. | |||||
| 11 | The ability to engage in life-long learning, including independent and continuous learning, adapting to new and emerging technologies, and thinking inquisitively regarding technological changes. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | 5 | 3 | 15 |
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 5 | 4 | 20 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 5 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 5 | 5 |
| Total Workload | 98 | ||