Numerical Linear Algebra II (AA4): Διαφορά μεταξύ των αναθεωρήσεων
Από Wiki Τμήματος Μαθηματικών
Γραμμή 75: | Γραμμή 75: | ||
! Delivery | ! Delivery | ||
| | | | ||
In the classroom | |||
|- | |- | ||
! Use of Information and Communications Technology | ! Use of Information and Communications Technology | ||
| | | - | ||
|- | |- | ||
! Teaching Methods | ! Teaching Methods | ||
Γραμμή 90: | Γραμμή 89: | ||
| 39 | | 39 | ||
|- | |- | ||
| | | Study and analysis of bibliography | ||
| | | 78 | ||
|- | |- | ||
| | | Exercises - Homework | ||
| | | 70.5 | ||
|- | |- | ||
| Course total | | Course total | ||
Γραμμή 102: | Γραμμή 101: | ||
! Student Performance Evaluation | ! Student Performance Evaluation | ||
| | | | ||
Written examination - Oral Examination | |||
|} | |} | ||
Αναθεώρηση της 10:22, 10 Νοεμβρίου 2022
Graduate Courses Outlines - Department of Mathematics
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 |