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

Gurantor departmentDepartment of Applied MathematicsCredits2
Subject guarantordoc. Ing. David Horák, Ph.D.Subject version guarantordoc. Ing. David Horák, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageCzech
Year of introduction2019/2020Year of cancellation2020/2021
Intended for the facultiesFEIIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
LUK76 doc. Ing. Dalibor Lukáš, Ph.D.
Extent of instruction for forms of study
Form of studyWay 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

Project work


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.


Other requirements

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


Subject has no prerequisities.


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 nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit Credit 100  51
Mandatory attendence parzicipation: Recommended attendance at tutorials is 80%.

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Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType 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 nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner