Elective Specialization courses (ΠΜ-E)
CS (Computer Science)
TEACHING HOURS per week
Foundations of Computer Science (Ε1):
Data and Knowledge Management (Ε2):
Hardware and Architecture (Ε4):
Communications and Networking (Ε5):
Signal and Information Processing (Ε6):
The course covers the design and analysis of numerical algorithms for Matrix computations. Matrix computations consist the core of problems in the Computational Science and Technology. In particular the syllabus of the course is the following
- Error analysis for numerical computations.
- Numerical solution of linear systems. Direct (Gaussian Elimination, Gauss-Jordan, LU Decomposition) and Iterative methods (SOR, SSOR, PSD, Semi-Iterative and Conjugate Gradient).
- Numerical computation of Eigenvalues and Eigenvectors (Jacobi, Givens, LR, QR and Householder)
- Least Squares Problem.
- Numerical solution of Polynomial equations (Bernoulli, Quotient-Difference, Muller, Steffensen, Graffe’s and Bairstow) and Non Linear systems of equations (Newton and Newton-SOR).
- Introduction to the numerical solution of Partial Differential equations.
LITERATURE AND STUDY MATERIALS - READING LIST
- Gene H. Golub, Charles F. Van Loan, Theory and Matrix Computations, John Hopkins University Press, 2015 (translated in greek, Publ. ).
- Nikolaos Missirlis, Numerical Analysis : An algorithmic approach, Publication by National and Kapodistrian University of Athens, 2017. Eudoxus Numerical Analysis