OPTIMIZATION

Course Information
TitleΒΕΛΤΙΣΤΟΠΟΙΗΣΗ / OPTIMIZATION
CodeNGE-06-02
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorNikolaos Tsitsas
CommonNo
StatusActive
Course ID40002959

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 56
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪYPOCΗREŌTIKO KATA EPILOGĪ635

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
600155641
Course Type 2016-2020
  • Background
  • General Knowledge
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Learning Outcomes
Cognitive: the students obtain knowledge of Optimization Techniques which are exploited in several branches of Computer Science. Skills: the students understand in depth Optimization Techniques which are necessary in several procedures and implementations of Decision Sciences and are applied in computational and communications systems.
General Competences
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
Course Content (Syllabus)
-- Introduction and motivation with examples and applications -- Basic notions. Optimization algorithms, theorems and rate of convergence -- Optimization of non-linear problems with and without constraints -- Implementations with mathematical software
Keywords
Nonlinear Optimization, Optimization Algorithms
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
Description
There is a web page for the course in elearning
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39
Reading Assigment30
Project48
Exams3
Literature Study30
Total150
Student Assessment
Description
There will be two tests and the students can pass the course if they have grade greater than 5 in each one of these tests. Also, projects will be assigned with bonus on the final grade from 0 to 2 points (depending on the quality each project).
Student Assessment methods
  • Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Μη γραμμικές μέθοδοι βελτιστοποίησης Μεθοδολογία και αλγόριθμοι, Βασιλείου Παναγιώτης - Χρήστος, Γεωργίου Αθανάσιος, 960-431-248-0, Ζήτη 1993, 1η έκδ., 11113. 2. Τεχνικές βελτιστοποίησης, Ροβιθάκης Γεώργιος Α., 978-960-418-141-4, Τζιόλα 2007, 1η εκδ., 18549025.
Additional bibliography for study
Σημειώσεις παραδόσεων του διδάσκοντα (120 σελίδες)
Last Update
11-10-2020