NUMERICAL METHODS AND APPLICATIONS B
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Delivery method
- Teaching methods
- Contacts/Info
Basic course in numerical analysis.
Oral examination and project implemented in Matlab.
Students will acquire the basic knowledge in order to model and to solve ill-posed problems.
Discrete least squares: normal equations, QR factorization and SVD.
Classification, overfitting and stability, Tikhonov regularization and introduction to machine learning. Generalized cross-validation (GCV) and bilevel optimization methods.
Ill-posed problems: regularization, filtering methods, iterative regularizing methods, variational model, sparsity in the wavelet domain.
Convolution and Fast Fourier Tranform (FFT).
Applications: compression and denoising of signals and images, reconstruction of blurred signals and images, computerized tomography.
Discrete least-square problems: minimum norm solution, singular value decomposition (SVD), truncated SVD (TSVD), pseudo-inverse, Golub-Kahan algorithm to compute the SVD, Landweber iteration. (24 h)
Ill-posed problems and regularization: filtering by TSVD, Tikhonov method and SVD filtering. Strategies for estimating the regularization parameter: discrete Picard condition, discrepancy principle, L-curve and GCV. Regularization iterative methods (Landweber method and conjugate grandients for normal equations). Convolution and discrete Fourier transform by fast Fourier transform. Compression, segmentation and deconvolution of signals and images. (30 h)
Introduction to machine learning showing that the main idea of machine learning can be
traced back to the regularization of a ill-posed inverse problem
depending on the the choice of the data fidelity measure and the predicting
model. (10 h)
The numerical methods and some applications will be implemented and tested using MatLab.
Front lectures with some computer lab supplementary lessons.
Meeting by appointment.
Professors
Borrowers
-
Degree course in: MATHEMATICS