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Numerical methods of Linear Algebra for Sparse Matrices
В начало
Курсы
Осенний семестр
Магистратура
Num Methods 2025
Module 1. Background in matrix theory and sparse l...
Lecture notes for Module 1
Lecture notes for Module 1
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for_lecture_October8_orthogonal_linear ind_vectors.pdf
for_lecture_October8_QR-factorization.mlx
for_lecture_October22_jordan_schur.mlx
for_lecture_October22_LU_cholessky.mlx
for_lecture_October22_svd.mlx
for_lecture_October29_condition_number.mlx
for_lecture_October29_lu_fact_of_spd_matrix.mlx
Lecture 1.pdf
Lecture 2.pdf
Lecture 3.pdf
Lecture 4.pdf
◄ Working with Matlab in SFedU and getting started
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Course overview 2025
Course textbook
Working with Matlab in SFedU and getting started
Practical assignment 1. Getting started with Matlab
Practical assignment 2. Matrix fundamentals: types and structures
Practical assignment 3. Vector and matrix norms. Existence of solution, solving linear system in Matlab
Practical assignment 4. Gram-Schmidt and QR-factorization
Practical assignment 5. Eigenvalues and their multiplicities, matrix factorizations, Hermitian and positive definite matrices
Practical assignment 6. Schur, LU- and Cholessky factorizations of Hermitian positive definite matrices. Solving linear systems using LU- and Cholessky factorizations.
Practical assignment 7. Condition number, computational costs, well- and ill-conditioned problems
Practical assignment 8. Permutations, reordering and fill-ins
Practical assignment 9. Sparse storage and sparse formats
Practical assignment 10. Comparison of direct and iterative methods for different sparse systems
Practical assignment 1. Getting started with Matlab ►