Numerical Linear Algebra II (AA4): Διαφορά μεταξύ των αναθεωρήσεων
Από Wiki Τμήματος Μαθηματικών
Χωρίς σύνοψη επεξεργασίας |
|||
(4 ενδιάμεσες αναθεωρήσεις από τον ίδιο χρήστη δεν εμφανίζεται) | |||
Γραμμή 1: | Γραμμή 1: | ||
[[ | * [[Αριθμητική Γραμμική Άλγεβρα II (ΑΑ4)|Ελληνική Έκδοση]] | ||
{{Course-Graduate-Top-EN}} | |||
{{Menu-OnAllPages-EN}} | |||
=== General === | === General === | ||
Γραμμή 107: | Γραμμή 109: | ||
<!-- In order to edit the bibliography, visit the webpage --> | <!-- In order to edit the bibliography, visit the webpage --> | ||
<!-- https://wiki.math.uoi.gr/index.php/%CE%A0%CF%81%CF%8C%CF%84%CF%85%CF%80%CE%BF: | <!-- https://wiki.math.uoi.gr/index.php/%CE%A0%CF%81%CF%8C%CF%84%CF%85%CF%80%CE%BF:MAM150-Biblio --> | ||
{{ | {{MAM150-Biblio}} |
Τελευταία αναθεώρηση της 05:15, 16 Ιουνίου 2023
- Ελληνική Έκδοση
- Graduate Courses Outlines
- Outline Modification (available only for faculty members)
- Department of Mathematics
- Save as PDF or Print (to save as PDF, pick the corresponding option from the list of printers, located in the window which will popup)
General
School | School of Science |
---|---|
Academic Unit | Department of Mathematics |
Level of Studies | Graduate |
Course Code | AA4 |
Semester | 1 |
Course Title | Numerical Linear Algebra II |
Independent Teaching Activities | Lectures (Weekly Teaching Hours: 3, Credits: 7.5) |
Course Type | Special Background |
Prerequisite Courses | - |
Language of Instruction and Examinations |
Greek |
Is the Course Offered to Erasmus Students | Yes (in Greek) |
Course Website (URL) | See eCourse, the Learning Management System maintained by the University of Ioannina. |
Learning Outcomes
Learning outcomes |
After successful end of this course, students will be able to:
|
---|---|
General Competences |
|
Syllabus
Numerical methods for the computation of Eigenvalues and Eigenvectors: Power Method, QR Method, Stable algorithms (Howsholder Reflections, Givens Rotations). Singular Values: Singular Value Decomposition. Krylov subspace Methods for the solution of Large Scale Linear Systems: Preconditioned Conjugate Gradient Method. Generalized Minimal Residual Method (GMRES): Theory of Orthogonalization of Krylov Subspaces, Arnoldi and Lanczos Algorithms. Applications of Iterative Methods to boundary value problems and to Signal and Image Processing.
Teaching and Learning Methods - Evaluation
Delivery |
In the classroom | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Use of Information and Communications Technology | - | ||||||||||
Teaching Methods |
| ||||||||||
Student Performance Evaluation |
Written examination - Oral Examination |
Attached Bibliography
- “Αριθμητική Γραμμική Άλγεβρα”, Β. Δουγαλής, Δ. Νούτσος, Α. Χατζηδήμος, Τυπογραφείο Πανεπιστημίου Ιωαννίνων.
- “Matrix Computations”, G. H. Golub, C. F. Van Loan, The John Hopkings University Press, Baltimore and London, 1996.