Numerical Linear Algebra

  • Class 45
  • Practice 13
  • Independent work 92
Total 150

Course title

Numerical Linear Algebra

Lecture type


Course code






Lecturers and associates

Course objectives

Symmetric and i Hermitian (self-adjoint) matrices. Gramm-Schmidt's orthogonalization. Orthogonal and unitary matrices.
Diagonalization of a matrix. Spectral theorem for symmetric matrices; Nilpotent matrices. Jordan cells. Minimal polynomial.
Nilpotent matrices. Jordan cells. Minimal polynomial; Positively (negatively) semidefinite matrices. Positively (negatively) definite matrices. Indefinite matrices.
Quadratic forms and their diagonalization. Definite and indefinite quadratic forms. Signature. Minimum, maximum and saddle points of a quadratic form.
Operator norms. The space L(X,Y). Matrix norm. Convergence of matrices. Series of matrices; Neumann series. Spectral radius and spectral norm.
Projectors. QR factorization; HouseHolder triangularization and its stability..
Singular Value Decomposition and its application; Algorithms for the SVD.
Midterm exam.
Jacobi, Gauss-Seidel and Relaxation Methods; Convergence Results for Jacobi, Gauss-Seidel and Relaxation Methods.
Symmetric Form of the Gauss-Seidel and SOR Methods. Implementation Issues.
Conjugate Gradient Method and other Krylov Subspace Iterations; Applications: Analysis of an Electric Network. Finite Difference Analysis of Beam Bending.
Geometrical Location of the Eigenvalues; Stability and Conditioning Analysis; The Power Method. Inverse Iteration.
The QR Method; Hessenberg Reduction. Tridiagonal and Bidiagonal Reductions.; QR Iterations with Implicit Shifts..
Methods for Eigenvalues of Symmetric Matrices (The Jacobi's Method. Tridiagonal QR Iteration, Rayleigh Quotient Iteration); Applications: Free Dynamic Vibration of a Bridge.
Final exam.

Required reading

(.), A. Aglić Aljinović, N. Elezović, D. Žubrinić, Linearna algebra, Element, Zagreb, 2011.,
(.), Z. Drmač i ostali, Numerička analiza (predavanja i vježbe), Zagreb, 2003.,
(.), N. Truhar, Numerička linearna algebra, Osijek, 2012.,
(.), G. H. Golub i C. F. Van Loan: Matrix Computations, 3rd Edition, John Hopkins University Press, Baltimore, Maryland, 1996.,
(.), L.N. Trefethen, D. Bau: Numerical Linear Algebra, SIAM, 1997.,

Minimal learning outcomes

  • Define and describe fundamental concepts such as matrix norms, singular and eigen values and vectors
  • Recognize types of matrices, such as ortogonal, unitary, symetric, Hermitian, normal and positive definite matrices
  • Describe Jordan and Schure form of the matrix and a notion of a diagonalizable matrix
  • Apply matrix transformation in order to transform a matrix into triangular, Hessenberg and tridiagonal form
  • Derive and utilize SVD and QR factorization of the matrix for efficiently solving problems in practice
  • Analyze and employ iterative methods for linear algebraic systems
  • Explain and employ iterative algorithms for computing eigenvalues
  • Relate the quality of obtained numerical solution to derived theoretical results
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