# Numerical Linear Algebra for Signal Processing

**Lecturer:** Michael Joham with Sadaf Syed

**Target Audience:** Master EI and MSCE

**Language:** English

**Next Exam:** tbd.

**Additional Information:** TUMonline and Moodle

### Lectures/Tutorials in Summer Semester 2024

### Content

Introduction to the fundamentals of numerical linear algebra with the application to signal processing problems.

*Floating-point arithmetics:* IEEE standard, error of floating-point arithmetic.

*Preliminaries from linear algebra:* singular value decomposition (SVD), projectors, matrix norms, Householder reflection, Givens rotation.

*QR factorization:* Gram-Schmidt orthogonalization, Householder triangularization, applications of QR factorization. Back substitution: solving a triangular equation system, inversion of a triangular matrix and application to channel equalization.

*Least squares:* least squares with Cholesky factorization, QR factorization, and SVD; rank-deficient least squares, application to least squares estimation.

*Condition of a problem:* norm-wise & component-wise condition numbers, condition number of basic operations, condition of inner product, matrix-vector product, unitary matrix, and equation system.

*Stability of an algorithm:* backward stability, accuracy based on backward stability; stability of floating-point arithmetic, algebraic operations, Householder triangularization, back substitution; stability of solving equation systems via Householder triangularization.

*Systems of equations:* Gaussian elimination, pivoting, stability of Gaussian elimination; Cholesky factorization, pivoting, stability.

*Eigenvalues:* Hessenberg form; Rayleigh quotient iteration, QR algorithm, application to principal component analysis; SVD, bi-diagonal form, implicit Q-theorem, Golub-Reinsch SVD, application to blind channel estimation.