数理アルゴリズム特論

数理アルゴリズム特論_E
Instructor(s)	Tetsuya Sakurai, Hiroto Tadano, Akira Imakura
E-Mail	sakurai [AT] cs.tsukuba.ac.jp, tadano [AT] cs.tsukuba.ac.jp, imakura [AT] cs.tsukuba.ac.jp
URL
Office hours	SB1022 Fri. 10:00-11:00
Cource#	01CH103, 02RA215
Area	Information Mathematics and Modeling
Basic/Advanced
Course style	Lecture
Term	FallAB
Period	Fri3,4
Room#	SB1001
Keywords	Computational science, large-scale linear algebra computations
Prerequisites	Basic knowledge of linear algebra and programming
relation degree program competence	Knowledge Utilization Skills,International Skills,Research Skills,Expert Knowledge
Goal
Outline	Lectures on algorithms and modeling that emerge in scientific computing, focusing particularly on large-scale linear computation.
Course plan	Learning to model physical phenomena, with example applications (Weeks 1 and 2) Partial differential equations, discretization, boundary conditions Numerical Methods and Computational Tools Mastering fundamentals relating to matrix operations (Weeks 3 and 4) Matrix norms, BLAS, LAPACK, sparse matrices Understanding iterative solution techniques for linear equations (Weeks 5 and 6) Krylov subspace methods, preconditioning Understanding eigenvalue solution methods (Weeks 7 and 8) Dense matrix solution techniques, sparse matrix solution methods Understanding parallelization of numerical computing methods (Weeks 9 and 10) Vector operations, matrix ordering, region segmentation
Textbook
References	T. Sakurai, An Introduction to Numerical Methods with MATLAB and Scilab, University of Tokyo Press., Tokyo (2003) (in Japanese)
Evaluation	Grades are assessed using three reports (33.3 points each) as follows: A+ (90 - 100) A (80 - 90) B (70 - 80) C (60 - 70) D (- 60)
TF / TA
Misc.	奇数年度開講。