Computational Methods for Linear Inverse Problems


Instructor & Tutor

Textbook & Reference books

  1. (textbook) Discrete Inverse Problems: Insight and Algorithms, Per Christian Hansen
  2. Computational Methods for Inverse Problems, Curtis R. Vogel
  3. Numerical Linear Algebra, Lloyd N. Trefethen and David Bau, III
  4. Deblurring Images: Matrices, Spectra, and Filtering, Per Christian Hansen, James G. Nagy, and Dianne P. O'Leary
  5. Rank-Deficient and Discrete ill-Posed Problems: Numerical Aspects of Linear Inversion, Per Christian Hansen
  6. Matrix Methods in Data Mining and Pattern Recognition, Lars Elden
  7. Introduction to Scientific Computing and Data Analysis, Mark H. Holmes
  8. MATLAB Primer, Timothy A. Davis

Lecture contents

Grading policy


  1. Write solutions/programming reports in english and latex/matlab (you can use matlab's 'publish'). Only one pdf-file should be submitted with filename studentnumberAx.pdf for Assignment x or studentnumberPx.pdf for Programming x. For example, if your student number is 19020140000000, then your submission for Assignment 1 is 19020140000000A1.pdf, and your submission for Programming 1 is 19020140000000P1.pdf. Submissions in other file formats are unacceptable!
  2. Discussion is encouraged. However, transcribed solutions and copied programs are both unacceptable!
  3. Submit your homework to: Late submissions get only half the score. Unaccepted submissions get 0 score. No exceptions!

Reading project (tentative)

  1. Martin Fuhry and Lothar Reichel, A new Tikhonov regularization method, NA, 2012
  2. Silvia Noschese and Lothar Reichel, A modified truncated singular value decomposition method for discrete ill-posed problems, NLAA, 2014
  3. Michiel E. Hochstenbach, Lothar Reichel, and Giuseppe Rodriguez, Regularization parameter determination for discrete ill-posed problems, JCAM, 2015