After a very refreshing and enjoying summer vacation I am back to work and back to blogging. This week there is the ILAS 11 (where ILAS stands for International Linear Algebra Society) here at TU Braunschweig; and since the talks take place in the building just next to where I am, I enjoyed some of them. Two talks have been especially interesting to me.

The first one was Tikhonov Regularization for Large Scale Inverse Problems by Melina Freitag (maybe the first link is not working yet but Melina said that she is going to upload her slides under that address). She talked about the ways the weather forecast is done these days in the UK and in Europe and especially on the process of Data Assimilation where one uses the previous weather forecasts and newly arrived measurements to produce a better estimate of the state. As a matter of fact, two popular methods use in this field (3dVar and 4dVar) are equivalent to classical Tikhonov regularization. In her section on ${L^1}$-penalties (which I usually call ${\ell^1}$-penalties…) she actually introduced a kind of discrete ${TV}$-penalty as a replacement for the usual quadratic (${\ell^2}$) penalty. Her motivation was as usual: Tikhonov regularization smoothes too much and weather fronts are not smooth. She did not have results of this kind of ${TV}$ regularization as a replacement in 4dVar with real weather data but with smaller toy examples (with non-linear advection equations) since the resulting optimization problem is LARGE. However, her results look promising. I am not sure if she did, but one was tempted to arrive at the conclusion that “4dVar with ${TV}$ penalty gives a better resolution of weather fronts”. It happened that during her talk there was thunderstorm with heavy rain in front of the windows which has not been predicted by the forecast (according to which, the thunderstorm should happen the next day). Now: Would a ${TV}$ penalty be able to predict this thunderstorm for the right time? I am not sure. While ${TV}$ penalties do enforce edges, the precise place of the edge is still not too sure. My feeling is, that the accuracy of the position is better, the less the curvature of the edge is, but in general this highly depends on the ill-posed problem at hand.

The second talk was Recent Progress in the Solution of Discrete Ill-Posed Problems by Michiel Hochstenbach. The talk was pretty amusing; I especially liked the slogan for discrete ill-posed problems:

How to wisely divide by zero.

Also he introduced the three forms of variational regularization which I usually call Tikhonov, Ivanov and Morozov regularization (on slide 34) and introduced the Pareto front (under the name it usually has in discrete ill-posed problems: L-curve).

Another appealing slogan was:

Discrete ill-posed problems are essentially underdetermined.

(Since we do not want to solve the respective equation exactly, we usually have a lot of approximate solution of which we have to choose the one we like the most. Of course this is the same for continuous ill-posed problems.) As a consequence: One should use as much prior knowledge as possible.

In the rest of his talk he talked about improvements of the classical Tikhonov regularization (which is a linear method) by other linear methods with respect to both reconstruction quality and computational burden. He motivated his SRSVD (subspace restricted SVD) by the observation that the simple truncated SVD is often failing due to the fact that the first singular vectors do not contain useful information of the solution. He proposed to use a selected set of (orthonormal) vectors and use a restricted SVD in the following. However, does this use the knowledge that the solution shall be a linear combination of these selected vectors? And wouldn’t it be beneficial to use a larger set of (non-orthonormal) vectors, put into a (possible overcomplete) dictionary and use a sparse reconstruction method such a ${\ell^1}$ regularization which automatically selects the relevant vectors? He also proposed the so called “linear combination approach” which basically takes several outputs of various methods and search within the linear combinations of this outputs for a better solution. To do so he proposed to use another Ivanov-type regularization (slide 79). I still did not get why he uses the largest available norm as a constraint here… However there should be an answer somewhere is his papers.

Edit: Michiel sent me the following answer:

I used the largest available norm, since the norms of many solution approaches are often smaller than, or approximately equal to the true norm. In the paper Discrete ill-posed least-squares problems with a solution norm constraint we reach the conclusion that
“For the approach based on the solution norm constraint, it seems important that $\|x\|$ not be underestimated.”

Edit: The above mentioned paper Discrete ill-posed least-squares problems with a solution norm constraint is to be published in “Linear Algebra and its Applications”. It can be found via its doi.