470-2211/01 – Numerical linear algebra 2 (NLA2)

Gurantor department	Department of Applied Mathematics	Credits	2
Subject guarantor	doc. Ing. David Horák, Ph.D.	Subject version guarantor	doc. Ing. David Horák, Ph.D.
Study level	undergraduate or graduate	Requirement	Compulsory
Year	2	Semester	winter
		Study language	Czech
Year of introduction	2019/2020	Year of cancellation	2020/2021
Intended for the faculties	FEI	Intended for study types	Bachelor

Instruction secured by
Login	Name	Tuitor	Teacher giving lectures
LUK76	doc. Ing. Dalibor Lukáš, Ph.D.

Extent of instruction for forms of study
Form of study	Way of compl.	Extent
Full-time	Credit	0+2
Part-time	Credit	0+8

Subject aims expressed by acquired skills and competences

Linear algebra is behind the solution of large-scale engineering problems. In Numerical linear algebra 2 students get familiar with modern software libraries with efficient sequential and parallel implementations of linear algebra algorithms and with their applications.

Teaching methods

Tutorials
Project work

Summary

The subject's core consists in effiecient parallel linear algebra algorithms exploiting fully the computational power of nowadays supercomputers.

Compulsory literature:

- G. H. Golub, C. F. Van Loan - Matrix computations. Johns Hopkins University Press, 2012. - PETSc Users Manual, http://www.mcs.anl.gov/petsc/petsc-current/docs/manual.pdf

Recommended literature:

- Y. Saad - Iterative methods for sparse linear systems. SIAM, 2003.

Way of continuous check of knowledge in the course of semester

Continuous study verification: • Test - max. 60 points. • Individual project on parallel implementation of chosen algorithm - max. 40 points. Requirements for passing the subject: • Writing the test. • Submitting and successful defense of a project.

E-learning

Other requirements

Successful defense of a semestral project. Students are familiar with fundamentals of linear algebra.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Outline: 1. Numerical libraries overview 2. Basic operations with parallel objects in PETSc - vectors, matrices, index sets 3. Direct solvers and their parallelization - multifrontal vs. supernodal methods 4. Iterative solvers in PETSc - KSP 5. Spectral analysis using SLEPc 6. Preconditioning

Conditions for subject completion

Part-time form (validity from: 2019/2020 Winter semester, validity until: 2020/2021 Summer semester)

Task name	Type of task	Max. number of points (act. for subtasks)	Min. number of points	Max. počet pokusů
Credit	Credit	100	51	3

Mandatory attendence participation: Recommended attendance at tutorials is 80%.

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic year	Programme	Zaměření	Form	Study language	Tut. centre	Year	Type of duty
2019/2020	(B0541A170008) Computational and Applied Mathematics	VMI	P	Czech	Ostrava	2	Compulsory	study plan
2019/2020	(B0541A170008) Computational and Applied Mathematics	VMI	K	Czech	Ostrava	2	Compulsory	study plan
2019/2020	(B0541A170009) Computational and Applied Mathematics	VMI	P	English	Ostrava	2	Compulsory	study plan

Occurrence in special blocks

Block name	Academic year	Form of study	Study language	Year	W	S	Type of block	Block owner

Assessment of instruction

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