bio | website | regularize.wordpress.com |
---|---|---|
location | Braunschweig, Germany | |
age | 36 | |
visits | member for | 4 years, 7 months |
seen | 21 hours ago | |
stats | profile views | 1,941 |
Professor at TU Braunschweig.
Area: Inverse problems, regularization theory, applied functional analysis, mathematical image processing.
Jan 9 |
comment |
Are there any with Erdös number 1 on mathoverflow?
Evidence: "Coloring graphs with locally few colors" by P. Erdõs, Z. Füredi, A. Hajnal, P. Komjáth, V. Rödl, Á. Seress, Discrete Math., 59(1986), cs.elte.hu/~kope/localcoloring.pdf |
Jan 5 |
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What is the advantage of the knowledge of jumps for approximating a function with trigonometric polynomials?
For the first method there is no convergence in the sup-norm (Gibbs phenomenon). |
Jan 4 |
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distribution discretization
I suspect that you will not have approximation in the total variation distance but only weak approximation. Probably this can be quantified by Prokhorov or Wasserstein metrics but I don't know a good pointer... |
Jan 4 |
revised |
Does removing some constraints in convex program change the optimal solution?
added 98 characters in body |
Jan 4 |
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Does removing some constraints in convex program change the optimal solution?
Jupp, this is also true in two variables. |
Jan 4 |
answered | Does removing some constraints in convex program change the optimal solution? |
Dec 29 |
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Finding sparsest solution of a linear system
Some good heuristics go under the name "pursuit", e.g. (regularized) (orthogonal) matching pursuit or basis pursuit. |
Dec 26 |
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An inequality concerning restricted isometry property
If you only have $supp(x) \subset S_1$ and $supp(y) \subset S_2$ then no lower bound greater than zero is to be expected and even if you have non-trivial intersection of the supports then the inner product can well be zero, I guess. |
Dec 21 |
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Open problems in compressed sensing
One thing is that it makes a huge difference if one answers this question as a mathematician or as an engineer. Well, of course this is site for mathematics but the idea of compressed sensing is really a practical one: get information about a high dimensional object that lives in lower dimensional but nonlinear subspace from few measurements. The meaning of all ingredients is debatable here and varies a lot from problem to problem. Hence one open problem is to find the right mathematical formulation of the problem... |
Dec 19 |
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Image of (right) inverse trace map $\xi\colon H^{\frac 12}(\partial\Omega) \to H^1(\Omega)$ dense in $H^1(\Omega)$?
Ok, right - I thought that I had a basic misconception about the problem… |
Dec 19 |
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journal to submit mathematic books' review
The German "Mathematische Semesterberichte" publishes such reviews (probably only in German). |
Dec 19 |
reviewed | Close How to construct a graph with arbitrarily large girth and large chromatic number? |
Dec 19 |
reviewed | Leave Open Image of (right) inverse trace map $\xi\colon H^{\frac 12}(\partial\Omega) \to H^1(\Omega)$ dense in $H^1(\Omega)$? |
Dec 19 |
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Image of (right) inverse trace map $\xi\colon H^{\frac 12}(\partial\Omega) \to H^1(\Omega)$ dense in $H^1(\Omega)$?
Isn't solving the respective Dirichlet problem saying that the image contains $H^{3/2}$? |
Dec 16 |
reviewed | Close Alexander duality theorem |
Dec 11 |
revised |
What is an extragradient method?
corrected spelling |
Dec 10 |
answered | Galerkin Projection on Integral Operators |
Dec 10 |
answered | What is an extragradient method? |
Dec 7 |
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Banach space of discontinuous functions(Killing continuous functions)
"Appropriate quotient space" Do you have an idea how this space looks like (or how the space which you divide by)? I guess the quotient contains all characteristic functions of all points and then it wouldn't be separable in the first norm. The second norm looks pretty weired to me… |
Dec 4 |
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$Ax=b$ in a function space
I am not sure what you are after. Do you want to show that the solution depends continuously on the matrix? Are you especially interested in the case where $A$ gets singular? |