Optimization Techniques
| Title | Τεχνικές Βελτιστοποίησης / Optimization Techniques |
| Code | 052 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter |
| Coordinator | Georgios Rovithakis |
| Common | Yes |
| Status | Active |
| Course ID | 600001001 |
Programme of Study: Electrical and Computer Engineering
Registered students: 116
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| ELECTRICAL ENERGY | Elective Courses | 7 | 4 | 5 |
| ELECTRONICS AND COMPUTER ENGINEERING | Elective Courses | 7 | 4 | 5 |
| TELECOMMUNICATIONS | Elective Courses | 7 | 4 | 5 |
| Academic Year | 2021 – 2022 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Class ID | 600196734
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600196734
- eLearning (Moodle):
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
1. Linear Algebra
2. Mathematical Calculus
3. Real Analysis
4. Numerical Analysis
Learning Outcomes
Upon completion of the course, the students will be able to:
a) design and analyze optimization problems;
b) design and analyze optimization algorithms for nonlinear problems, with or without constraints;
c) implement optimization algorithms in MATLAB.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
Basic principles (convex sets and functions, optimality conditions, duality);
Unconstrained optimization (line search with and without using derivatives, gradient methods, the Levenberg-Marquardt modification, quasi-newton and conjugate gradient methods);
Constrained optimization (penalty function methods, barrier function methods, methods of feasible directions, augmented Lagrangian methods);
Convergence and speed of convergence analysis;
Global optimization (simulated annealing, evolutionary algorithms).
Keywords
Convex optimization, nonlinear optimization, global search
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 33 | 1.1 | |||
| Tutorial | 33 | 1.1 | |||
| Written assigments | 60 | 2 | |||
| Exams | 24 | 0.8 | |||
| Total | 150 | 5 |
Student Assessment
Description
Written Examination
Assessment of optional projects
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Oral Exams (Summative)
- Written Exam with Problem Solving (Summative)
- Report (Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Γεώργιος Ροβιθάκης, Τεχνικές Βελτιστοποίησης, Εκδόσεις Τζίολα, 2007.
Additional bibliography for study
1. D.P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, Massachusetts, 1999.
2. M.S. Bazaraa, H.D. Sherali, C.M. Shetty, Nonlinear Programming Theory and Algorithms, John Wiley and Sons, New York, 1993.
Last Update
30-11-2020
