If you are working on optimization with partial differential equations as constraints, you may be interested in the website

“OPTPDE – A Collection of Problems in PDE-Constrained Optimization”, http://www.optpde.net.

If you have developed an algorithm which can handle a certain class of optimization problems you need to do evaluations and tests on how well the method performs. To do so, you need well constructed test problems. This could be either problems where the optimal solution is known analytically our problems where the solution is known with a rigorous error bound obtained with a bullet-proof solver. Both things are not always easy to obtain and OPTPDE shall serve as a resource for such problems. It has been designed by Roland Herzog, Arnd Rösch, Stefan Ulbrich and Winnifried Wollner.

The generation of test instance for optimization problems seems quite important to me and indeed, several things can go wrong if this is not done right. Frequently, one sees tests for optimization routines on problems where the optimal solution is not known. Since there are usually different ways to express optimality conditions it is not always clear how to check for optimality; even more so, if you only check for “approximate optimality”, e.g. up to machine precision. A frequently observed effect is a kind of “trusted method bias”. By this I mean that an optimal solution is calculated by some trusted method and comparing the outcome of the tested routine with this solution. However, the trusted method uses some stopping criterion usually based on some specific set of formulations of optimality conditions and these can be different from what the new method has been tuned to. And most often, the stopping criteria do not give a rigorous error bound for the solution or the optimal objective value.

For sparse reconstruction problems, I dealt with this issue in “Constructing test instance for Basis Pursuit Denoising” (preprint available here) but I think this methodology could be used for other settings as well.

Today there are several things I could blog on. The first is the planary by Rich Baraniuk on Compressed Sensing. However, I don’t think that I could reflect the content in a way which would be helpful for a potential reader. Just for the record: If you have the chance to visit one of Rich’s talk: Do it!

The second thing is the talk by Bernd Hofmann on source conditions, smoothness and variational inequalities and their use in regularization of inverse problems. However, this would be too technical for now and I just did not take enough notes to write a meaningful post.

As a third thing I have the talk by Christian Clason on inverse problems with uniformly distributed noise. He argued that for uniform noise it is much better to use an {L^\infty} discrepancy term instead of the usual {L^2}-one. He presented a path-following semismooth Newton method to solve the problem

\displaystyle \min_x \frac{1}{p}\|Kx-y^\delta\|_\infty^p + \frac{\alpha}{2}\|x\|_2^2

and showed examples with different kinds of noise. Indeed the examples showed that {L^\infty} works much better than {L^2} here. But in fact it works even better, if the noise is not uniformly distributed but “impulsive” i.e. it attains bounds {\pm\delta} almost everywhere. It seems to me that uniform noise would need a slightly different penalty but I don’t know which one – probably you do? Moreover, Christian presented the balancing principle to choose the regularization parameter (without knowledge about the noise level) and this was the first time I really got what it’s about. What one does here is, to choose {\alpha} such that (for some {\sigma>0} which only depends on {K}, but not on the noise)

\displaystyle \sigma\|Kx_\alpha^\delta-y^\delta\|_\infty = \frac{\alpha}{2}\|x_\alpha^\delta\|_2^2.

The rational behind this is, that the left hand side is monotonically non-decreasing in {\alpha}, while the right hand side is monotonically non-increasing. Hence, there should be some {\alpha} “in the middle” which make both somewhat equally large. Of course, we do neither want to “over-regularize” (which would usually “smooth too much”) nor to “under-regularize” (which would not eliminate noise). Hence, balancing seems to be a valid choice. From a practical point of view the balancing is also nice because one can use the fixed-point iteration

\displaystyle \alpha^{n+1} = 2\sigma\frac{\|Kx_{\alpha^n}^\delta - y^\delta\|_\infty}{\|x_{\alpha_n}^\delta\|_2^2}

which converges in a few number of iterations.

Then there was the talk by Esther Klann, but unfortunately, I was late so only heard the last half…

Last but not least we have the talk by Christiane Pöschl. If you are interested in Total-Variation-Denoising (TV denoising), then you probably have heard many times that “TV denoising preserves edges” (have a look at the Wikipedia page – it claims this twice). What Christiane showed (in a work with Vicent Caselles and M. Novaga) that this claim is not true in general but only for very special cases. In case of characteristic functions, the only functions for which the TV minimizer has sharp edges are these so-called calibrated sets, introduced by Caselles et el. Building on earlier works by Caselles and co-workers she calculated exact minimizers for TV denoising in the case that the image consists of characteristic functions of two convex sets or of a single star shaped domain, that is, for a given set B she calculated the solution of

\displaystyle \min_u\int (u - \chi_B)^2dx + \lambda \int|Du|.

This is not is as easy as it may sound. Even for the minimizer for a single convex set one has to make some effort. She presented a nice connection of the shape of the obtained level-sets with the morphological operators of closing and opening. With the help of this link she derived a methodology to obtain the exact TV denoising minimizer for all parameters. I do not have the images right now but be assured that most of the time, the minimizers do not have sharp edges all over the place. Even for simple geometries (like two rectangles touching in a corner) strange things happen and only very few sharp edges appear. I’ll keep you posted in case the paper comes out (or appears as a preprint).

Christiane has some nice images which make this much more clear:

For two circles edges are preserved if they are far enough away from each other. If they are close, the area “in between” them is filled and, moreover, obey this fuzzy boundary. I remember myself seeing effects like this in the output of TV-solvers and thinking “well, it seems that the algorithm is either not good or not converged yet – TV should output sharp edges!”.


For a star-shaped shape (well, actually a star) the output looks like this. The corners are not only rounded but also blurred and this is true both for the “outer” corners and the “inner” corners.


So, if you have any TV-minimizing code, go ahead and check if your code actually does the right things on images like this!
Moreover, I would love to see similar results for more complicated extensions of TV like Total Generalized Variation, I treated here.





Today I’d like to blog about two papers which appeared on the arxiv.

1. Regularization with the Augmented Lagrangian Method – Convergence Rates from Variational Inequalities

The first one is the paper “Regularization of Linear Ill-posed Problems by the Augmented Lagrangian Method and Variational Inequalities” by Klaus Frick and Markus Grasmair.

Well, the title basically describes the content quite accurate. However, recall that the Augmented Lagrangian Method (ALM) is a method to calculate solutions to certain convex optimization problems. For a convex, proper and lower-semicontinuous function {J} on a Banach space {X}, a linear and bounded operator {K:X\rightarrow H} from {X} into a Hilbert space {H} and an element {g\in H} consider the problem

\displaystyle   \inf_{u} J(u)\quad\text{s.t.}\quad Ku=g. \ \ \ \ \ (1)

The ALM goes as follows: Start with an initial dual variable {p_0}, choose step-sizes {\tau_k>0} and iterate

\displaystyle  u_k \in \text{argmin}\Big(\frac{\tau_k}{2}\|Ku-g\|^2 + J(u) + \langle p_{k-1},Ku-g\rangle\Big)

\displaystyle  p_k = p_{k-1}+\tau_k(g-Ku_k).

(These days one should note that this iteration is also known under the name Bregman iteration…). Indeed, it is known that the ALM converges to a solution of (1) if there exists one. Klaus and Markus consider the ill-posed case, i.e. the range of {K} is not closed and {g} is replaced by some {g^\delta} which fulfills {\|g-g^\delta\|\leq\delta} (and hence, {g^\delta} is generally not in the range of {K}). Then, the ALM does not converge but diverges. However, one observes “semi-convergence” in practice, i.e. the iterates approach an approximate “solution to {Ku=g^\delta}” (or even a true solution to {Ku=g}) first but then start to diverge from some point on. Then it is natural to ask, if the ALM with {g} replaced by {g^\delta} can be used for regularization, i.e. can one choose a stopping index {k^*} (depending on {\delta} and {g^\delta}) such that the iterates {u_{k^*}^\delta} approach the solution of (1) if {\delta} vanishes? The question has been answered in the affirmative in previous work by Klaus (here and here) and also estimates on the error and convergence rates have been derived under an additional assumption on the solution of (1). This assumption used to be what is called “source condition” and says that there should exist some {p^\dag\in H} such that for a solution {u^\dagger} of (1) it holds that

\displaystyle  K^* p^\dagger \in\partial J(u^\dagger).

Under this assumption it has been shown that the Bregman distance {D(u_{k^*}^\delta,u^\dag)} goes to zero linearly in {\delta} under appropriate stopping rules. What Klaus and Markus investigate in this paper are different conditions which ensure slower convergence rates than linear. These conditions come in the form of “variational inequalities” which gained some popularity lately. As usual, these variational inequalities look some kind of messy at first sight. Klaus and Markus use

\displaystyle  D(u,u^\dag)\leq J(u) - J(u^\dag) + \Phi(\|Ku-g\|^2)

for some positive functional {D} with {D(u,u)=0} and some non-negative, strictly increasing and concave function {\Phi}. Under this assumption (and special {D}) they derive convergence rates which again look quite complicated but can be reduced to simpler and more transparent cases which resemble the situation one knows for other regularization methods (like ordinary Tikhonov regularization).

In the last section Klaus and Markus also treat sparse regularization (i.e. with {J(u) = \|u\|_1}) and derive that a weak condition (like {(K^*K)^\nu p^\dag\in\partial J(u^\dag)} for some {0<\nu<1/2} already imply the stronger one (1) (with a different {p^\dag}). Hence, interestingly, it seems that for sparse regularization one either gets a linear rate or nothing (in this framework).

2. On necessary conditions for variational regularization

The second paper is “Necessary conditions for variational regularization schemes” by Nadja Worliczek and myself. I have discussed some parts of this paper alread on this blog here and here. In this paper we tried to formalize the notion of “a variational method” for regularization with the goal to obtain necessary conditions for a variational scheme to be regularizing. As expected, this goal is quite ambitions and we can not claim that we came up with ultimate necessary condition which describe what kind of variational methods are not possible. However, we could first relate the three kinds of variational methods (which I called Tikhonov, Morozov and Ivanov regularization here) and moreover investigated the conditions on the data space a little closer. In recent years it turned out that one should not always use a term like {\|Ku-g^\delta\|^2} to measure the noise or to penalize the deviation from {Ku} to {g^\delta}. For several noise models (like Poisson noise or multiplicative noise) other functionals are better suited. However, these functionals raise several issues: They are often not defined on a linear space but on a convex set, sometimes with the nasty property that their interior is empty. They often do not have convenient algebraic properties (e.g. scaling invariance, triangle inequalities or the like). Finally they are not necessarily (lower semi-)continuous with respect to the usual topologies. Hence, we approached the data space from quite abstract way: The data space {(Y,\tau_Y)} is topological space which comes with an additional sequential convergence structure {\mathcal{S}} (see e.g. here) and on (a subset of) which there is a discrepancy functional {\rho:Y\times Y\rightarrow [0,\infty]}. Then we analyzed the interplay of these three things {\tau_Y}, {\mathcal{S}} and {\rho}. If you wonder why we use the additional sequential convergence structure, remember that in the (by now classical) setting for Tikhonov regularization in Banach spaces with a functional like

\displaystyle  \|Ku-g^\delta\|_Y^q + \alpha\|u\|_X^p

with some Banach space norms {\|\cdot\|_Y} and {\|\cdot\|_X} there are also two kinds of convergence on {Y}: The weak convergence (which is replaced by {\tau_Y} in our setting) which is, e.g., used to describe convenient (lower semi-)continuity properties of {K} and the norm {\|\cdot\|_Y} and the norm convergence which is used to describe that {g^\delta\rightarrow g^\dag} for {\delta\rightarrow 0}. And since we do not have a normed space {Y} in our setting and one does not use any topological properties of the norm convergence in all the proofs of regularizing properties, Nadja suggested to use a sequential convergence structure instead.

On my way to ENUMATH 11 in Leicester I stumbled upon the preprint Multi-parameter Tikhonov Regularisation in Topological Spaces by Markus Grasmair. The paper deals with fairly general Tikhonov functionals and its regularizing properties. Markus considers (nonlinear) operators {F:X\rightarrow Y} between two set {X} and {Y} and analyzes minimizers of the functional

\displaystyle T(x) = S(F(x),y) + \sum_k \alpha_k R_k(x).

The functionals {S} and {R_k} play the roles of a similarity measure and regularization terms, respectively. While he also treats the issue of noise in the operator and the multiple regularization terms, I was mostly interested in his approach to the general similarity measure. The category in which he works in that of topological spaces and he writes:

“Because anyway no trace of an original Hilbert space or Banach space structure is left in the formulation of the Tikhonov functional {T} […], we will completely discard all assumption of a linear structure and instead consider the situation, where both the domain {X} and the co-domain {Y} of the operator {F} are mere topological spaces, with the topology of {Y} defined by the distance measure {S}.”

The last part of the sentence is important since previous papers often worked the other way round: Assume some topology in {Y} and then state conditions on {S}. Nadja Worliczek observed in her talk “Sparse Regularization with Bregman Discrepancy” at GAMM 2011 that it seems more natural to deduce the topology from the similarity measure and Markus took the same approach. While Nadja used the notion of “initial topology” (that is, take the coarsest topology that makes the functionals {y\mapsto S(z,y)} continuous), Markus uses the following family of pseudo-metrics: For {z\in Y} define

\displaystyle d^{(z)}(y,\tilde y) = |S(z,y)-S(z,\tilde y)|.

Unfortunately, the preprint is a little bit too brief for me at this point and I did not totally get what he means with “the topology {\sigma} induced by the uniformity induced by the pseudo-metric”. Also, I am not totally sure if “pseudo-metric” is unambiguous.. However, the topology he has in mind seems to be well suited in the sense that {y^n\rightarrow y} if {S(z,y^n)\rightarrow S(z,y)} for all {z}. Moreover, the condition that {S(z,y)=0} iff {z=y} implies that {\sigma} is Hausdorff. It would be good to have a better understanding on how the properties of the similarity measure are related to the properties of the induced topology. Are there examples in which the induced topology is both different from usual norm and weak topologies and also interesting?

Moreover, I would be interested, in the relations of the two approaches: via “uniformities” and the initial topology…

In this post I would like to comment on two papers I “stumbled upon”, one in regularization theory and one in image processing.

The first one is A regularization parameter for nonsmooth Tikhonov regularization by Kazufumi Ito, Bangti Jin and Tomoya Takeuchi. As the title announces, the paper addresses the problem of determining suitable regularization parameter for some kind of Tikhonov regularization. In particular, the authors propose a new heuristic method, i.e. method which does not use any estimate of the noise level in the data. This is an interesting and important topic for several reasons:

  1. Practically, estimates on the noise level are rarely available and if they are, they are not too reliable.
  2. Strictly speaking, these kind of rules are “bad” since there is the “Bakushinksii Veto”: Such rules only provide regularizations (e.g. in the sense of Engl, Hanke, Neubauer for problems which are well-posed (as a great service, the authors state and prove this statement as Theore 3.2).
  3. Despite this veto, several heuristic rules produce excellent results in practice.

Note that the last second points are not in contradiction. They merely say that the notion of “regularization” may be too strict. Usually, it uses a worst case estimate which may practically never observed.

The paper contributes a new rule and state that it is applicable to a broad range of problems. They use very general Tikhonov functional:

\displaystyle  \phi(x,y^\delta) + \eta\psi(x)

and do not assume that {\phi} or {\psi} are smooth. They use the value function

\displaystyle  F(\eta) = \min_x \phi(x,y^\delta) + \eta\psi(x)

and propose the following rule for {\eta}: For some {\gamma} choose {\eta} such that

\displaystyle  \Phi_\gamma(\eta) = \frac{F(\eta)^{1+\gamma}}{\eta}

is minimal. I do not have any intuition for this rule (however, from they proofs you see that they work, at least for “partially smooth cases”, see below). Lacking a name for this rule, one may use the term “weighted value function rule”.

They prove several nice properties of the value function (continuity, monotonicity and concavity) with loose assumptions on {\phi} and {\psi} (especially they do not even need existence of minimizers for {\phi(x,y^\delta) + \eta\psi(x)}, only that the minimum exists). However, when it comes to error estimates, they only obtain results for a specific discrepancy measure, namely a squares Hilbert space norm:

\displaystyle  \phi(x,y^\delta) = \tfrac12\|Kx-y^\delta\|^2.

It seems that, for general convex and lower-semicontinuous penalties {\psi} they build upon results from my paper with Bangti Jin on the Hanke-Raus rule and the quasi-optimality principle.

Another contribution of the paper is that it gives an algorithm that realizes the weighted value function rule (a thing which I omitted in my paper with Bangti). Their numerical experiments demonstrate that the weighted value function rule and the proposed algorithm works well for academic examples.

The next paper I want to discuss is the preprint Properties of {L^1-\text{TGV}^2}: The one-dimensional case by Kristian Bredies, Karl Kunisch and Tuomo Valkonen. There the authors analyze the somehow recent generalization “total generalized variation” {\text{TGV}} of the omnipresent total variation. The TGV has been proposed by Bredies, Kunisch and Pock in this paper recently and Kristian and me also briefly described it in our book on mathematical image processing. Loosely speaking, the TGV shall be a generalization of the usual total variation which does not lead to “staircasing”. While one may observe from the construction of the TGV functional, that staircasing is not to be expected, the authors in this paper give precise statements. By restricting to the one dimensional case they prove several interesting properties of the TGV functional, most notably that it leads to an equivalent norm of the space {BV}.

Maybe I should state the definitions here: The total variation of a function {u\in L^1(\Omega)} is

\displaystyle  \text{TV}(u) = \sup\{\int_\Omega u v'\ |\ v\in C^1_c(\Omega),\ \|v\|_\infty\leq 1\}

leading the the {BV}-norm

\displaystyle  \|u\|_{BV} = \|u\|_{L^1} + \text{TV}(u).

The {\text{TGV}^2} seminorm for a parameter tuple {(\alpha,\beta)} is

\displaystyle  \text{TGV}^2_{(\alpha,\beta)}(u) = \sup\{\int_\Omega u v''\ |\ C^2_c(\Omega), \|v\|_\infty\leq\beta,\ \|v'\|_\infty\leq\alpha\}

and the associated norm is

\displaystyle  \|u\|_{BGV^2} = \|u\|_{L^1} + \text{TGV}^2(u).

In Lemma 3.3 they prove that {\|\cdot\|_{BV}} and {\|\cdot\|_{BGV^2}} are equivalent norms on {\text{BV}}. In Section 4 they show that minimizers of

\displaystyle  \|u-f\|_{L^1} + \alpha\text{TV}(u)

obey staircasing of degree 0, i.e. the solution {u} is piecewise constant when it is not equal to {f}. For the minimizers of

\displaystyle  \|u-f\|_{L^1} + \text{TGV}^2_{(\alpha,\beta)}(u)

one has staircasing of degree 1: {u} is affine linear where it is not equal to {f}.

These two facts combined (norm equivalence of {\text{BV}} and {\text{BGV}^2} and the staircasing of degree 1) seem quite remarkable to me. They somehow show that staircasing is not related to the space {\text{BV}} of functions of bounded variation but only to the specific {\text{TV}} semi-norm. This is somehow satisfying since I still remember the thorough motivation of L. Rudin in his 1987 thesis for the usage of the space {\text{BV}} in image processing: If there where images which are not in {\text{BV}} we could not observe them. (He even draws an analogy to the question: How many angles can dance on the point of a needle?) Moreover, he further argues that {\text{BV}} is not too large in the sense that its elements are still accessible to analysis (e.g. in defining a weak notion of curvature although they may be discontinuous). The {\text{BGV}^2}-model shows that it is possible to overcome the undesired effect of staircasing while staying in the well founded and mathematically sound and appealing framework of {\text{BV}}.

The paper contains several more interesting results (e.g. on preservation of continuity and “affinity” and on convergence of with respect to {(\alpha,\beta)} which I do not collect here.

Coming back to regularization, especially Ivanov regularization. Recall that I used the term Ivanov regularization for the minimization problem

\displaystyle   \min S(Ax,y^\delta)\ \text{ s.t. }\ R(x)\leq \tau. \ \ \ \ \ (1)

I again stumbled upon some reference: It seems that in the case that the constraint {R(x)\leq \tau} defines a compact set, this method is usually referred to as “method of quasi solutions”. More precisely, I found this in “Elements of the theory of inverse problems” by A.M. Denisov, Chapter 6. There he uses metric spaces and proves the following:

Theorem 1 Let {X,Y} be metric spaces with metrics {d_X}, {d_Y}, respectively and {A:X\rightarrow Y} continuous. Furthermore let {M\subset X} be compact, {y^\dagger} be in the range of {A} and assume that {x^\dagger} is the unique solution of {Ax=y^\dagger} which lies in {M}. Finally for a {y^\delta} with {d_Y(y^\delta,y^\dagger)\leq\delta} define {X_\delta = \{x\ :\ d_Y(Ax,y^\delta)\leq\delta\}} and {X_\delta^M = X_\delta\cap M}. Then it holds for {\delta\rightarrow 0} that

\displaystyle  \sup_{x\in X_\delta^M}d_X(x,x^\dagger) \rightarrow 0.

Remark 1 Before we prove this theorem, we relate is to what I called Ivanov regularization above: The set {M} is encoded in~(1) as {M = \{x\ :\ R(x)\leq\tau\}} and the “discrepancy measure” {S} is simply the metric {d_Y}. Hence, let {x_M^\delta} denote a solution of

\displaystyle  \min\ d_Y(Ax,y^\delta)\ \text{ s.t. } x\in M.

Because {x^\dagger} is feasible for this problem it follows from {d_Y(Ax^\dagger,y^\delta) = d_Y(y^\dagger,y^\delta)\leq\delta} that {d_Y(Ax_M^\delta,y^\delta)\leq\delta}. Hence, {x_M^\delta\in X_M^\delta}. In other words: Ivanov regularization produces one element in the set {X_M^\delta}. Now, the theorem says that every element in {X_M^\delta} is a good approximation for {x^\dagger} (at least asymptotically).

Proof: We take a sequence {\delta_n\rightarrow 0} and assume to the contrary that there exist {\epsilon>0} such that for every {n} there exists {x_{\delta_n}\in X_M^{\delta_n}} such that it holds that {d_X(x_{\delta_n},x^\dagger)\geq \epsilon}. Since all {x_{\delta_n}} lie in {M} which is compact, there is a convergent subsequence {(x_k)} with limit {\bar x}. We obtain {d_X(\bar x,x^\dagger)\geq \epsilon}. However, this contradicts the assumption: d_Y(A\bar x,Ax^\dagger) & = &d_Y(A\bar x,y^\dagger) = \lim_{n\rightarrow \infty} d_Y(Ax_{\delta_n},y^\dagger) \nonumber
& \leq &\lim_{n\rightarrow \infty} d_Y(Ax_{\delta_n},y^{\delta_n}) + d_Y(y^{\delta_n},y^\dagger) \leq \lim_{n\rightarrow\infty}2\delta_n =0. \Box

Coming back to the interpretation of the Theorem~1 and Ivanov regularization: Instead of Ivanov regularization, one could also use the following feasibility problem: Find an {x} such that both {d_Y(Ax,y^\delta)\leq\delta} and {x\in M}. For the case of vector spaces {X} and {Y} and a convex set {M}, this would be a convex feasibility problem which one may attack by available methods.

A further important remark is that we did not assume any linearity on {A} (of course: we did not even assume a linear structure on {X} or {Y}). Hence, the theorem seem very powerful: There is no regularization parameter involved and one still gets convergence to the true solution! However, one of the assumptions in the theorem is somehow strong: The uniqueness of {x^\dagger}. To illustrate this we consider a special case:

Example 1 Let {X} and {Y} be real (or complex) vector spaces and {A} be linear with non-trivial null space. Furthermore, assume that {M\subset X} is convex and compact and consider scaled versions {\tau M} for {\tau>0}. Then the set of solutions of {Ax=y^\dagger} is an affine space in {X} and there are three cases for the intersection of this set and {\tau M}:

  1. The intersection is empty.
  2. The intersection is a convex set and contains infinitely many elements.
  3. The intersection contains exactly one element.

The last case occurs precisely, when the affine space of solution is tangential to {\tau M}. Loosely speaking, one may say that this case only occurs, if the set {M} is scaled precisely to the right size such that is only touches the affine space of solutions.

Another strong assumption in Theorem~1 is that the set {M} is compact. First there is a way to somehow relax this condition. Basically, we only need compactness to obtain the converging subsequence. Hence, one could try to work with a weaker topology on {Y} (which would result in a weaker notion of compactness) and then obtain a limit of a subsequence which converges in the weaker sense only. Then one would need some tool to deduce that the weak limit is indeed a solution. This strategy work, for example in Banach spaces:

Example 2 Let {X} and {Y} be reflexive Banach spaces and {A:X\rightarrow Y} be linear, bounded and one-to-one. We use the set {M = \{x\ :\ \|x\|_X\leq R\}} as prior knowledge on the solution of {Ax=y^\dagger}. Moreover, we use the metrics induced the norms of {X} and {Y}, respectively: {d_X(x,x') = \|x-x'\|_X} and {d_Y(y,y') = \|y-y'\|_Y}.

Obviously, {M} is not compact (if {X} is of infinite dimension) but it is weakly compact (and by the Eberlein-Smulian theorem also weakly-sequentially compact). In the situation of the proof of Theorem~1 we only get a weakly converging subsequence {x_{\delta_n}\rightharpoonup \bar x}. However, a linear operator {A} is also weak-to-weak linear, and hence {Ax_{\delta_n}\rightharpoonup A\bar x}. While we only have a weakly converging sequence, we still can obtain the contradiction in~(0) since the norm is weakly lower semicontinuous.

Another way to justify the assumption that the solution is in a known compact set, is that in practice we always use a representation of the solution which only use a finite number of degrees of freedom (think of a Galerkin ansatz for example). However, this interpretation somehow neglects that we are interested in finding the true solution to the true infinite dimensional problem and that the discretization of the problem should be treated as a different issue. Just building on the regularizing effect of discretization will almost surely result in a method which stability properties depend on the resolution of the discretization.

Finally: Another good reference of these somehow ancient results in regularization theory is one of the first books on this topics: “Solutions of ill-posed problems” by Tikhonov and Arsenin (1977). While it took me some time to get used the type of presentation, I have to admit that it is really worth to read this book (and other translation of Russian mathematical literature).

Some time ago I picked up the phrase Ivanov regularization. Starting with an operator A:X\to Y between to Banach spaces (say) one encounters the problem of instability of the solution of Ax=y if A has non-closed range. One dominant tool to regularize the solution is called Tikhonov regularization and consists of minimizing the functional \|Ax - y^\delta\|_Y^p + \alpha \|x\|_Y^q. The meaning behind these terms is as follows: The term \|Ax -y^\delta \|_Y^p is often called discrepancy and it should be not too large to guarantee, that the “solution” somehow explains the data. The term \|x\|_Y^q is often called regularization functional and shall not be too large to have some meaningful notion of “solution”. The parameter \alpha>0 is called regularization parameter and allows weighting between the discrepancy and regularization.

For the case of Hilbert space one typically chooses p=q=2 and gets a functional for which the minimizer is given more or less explicitly as

x_\alpha = (A^*A + \alpha I)^{-1} A^* y^\delta.

The existence of this explicit solution seems to be one of the main reasons for the broad usage of Tikhonov regularization in the Hilbert space setting.

Another related approach is sometimes called residual method, however, I would prefer the term Morozov regularization. Here one again balances the terms “discrepancy” and “regularization” but in a different way: One solves

\min \|x\|_X\ \text{s.t.}\ \|Ax-y^\delta\|_Y\leq \delta.

That is, one tries to find an x with minimal norm which explains the data y^\delta up to an accuracy \delta. The idea is, that \delta reflects the so called noise level, i.e. an estimate of the error which is made during the measurment of y. One advantage of Morozov regularization over Tikhonov regularization is that the meaning of the parameter \delta>0 is much clearer that the meaning of \alpha>0. However, there is no closed form solution for Morozov regularization.

Ivanov regularization is yet another method: solve

\min \|Ax-y^\delta\|_Y\ \text{s.t.}\ \|x\|_X \leq \tau.

Here one could say, that one wants to have the smallest discrepancy among all x which are not too “rough”.

Ivanov regularization in this form does not have too many appealing properties: The parameter \tau>0 does not seem to have a proper motivation and moreover, there is again no closed form solution.

However, recently the focus of variational regularization (as all these method may be called) has shifted from using norms to the use of more general functionals. For example one considers Tikhonov in an abstract form as minimizing

S(Ax,y^\delta) + \alpha R(x)

with a “general” similarity measure S and a general regularization term R, see e.g. the dissertation of Christiane Pöschl (which can be found here, thanks Christiane) or the works of Jens Flemming. Prominent examples for the similarity measure are of course norms of differences or the Kullback-Leibler divergence or the Itakura-Saito divergence which are both treated in this paper. For the regularization term one uses norms and semi-norms in various spaces, e.g. Sobolev (semi-)norms, Besov (semi-)norms, the total variation seminorm or \ell^p norms.

In all these cases, the advantage of Tikhonov regularization of having a closed form solution is not there anymore. Then, the most natural choice would be, in my opinion, Morozov regularization, because one may use the noise level directly as a parameter. However, from a practical point of view one also should care about the problem of calculating the minimizer of the respective problems. Here, I think that Ivanov regularization is important again: Often the similarity measure S is somehow smooth but the regularization term R is nonsmooth (e.g. for total variation regularization or sparse regularization with \ell^p-penalty). Hence, both Tikhononv and Morozov regularization have a nonsmooth objective function. Somehow, Tikhonov regularization is still a bit easier, since the minimization is unconstrained. Morozov regularization has a constraint which is usually quite difficult to handle. E.g. it is usually difficult (is it probably even ill posed?) to project onto the set defined by S(Ax,y^\delta)\leq \delta. Ivanov regularization has a smooth objective functional (at least if the similarity measure is smooth) and a constraint which is usually somehow simple (i.e. projections are not too difficult to obtain).

Now, I found, that all thee methods, Tikhonov, Morozov and Ivanov regularizazion are all treated in the book “Theory of linear ill-posed problems and its applications” by V. K. Ivanov,V. V. Vasin and Vitaliĭ Pavlovich Tanana in section 3.2, 3.3 and 3.4 respectively. Ivanov regularization goes under the name “method of quasi solutions” (section 3.2) and Morozov regularization is called “Method of residual”(section 3.4). Well, I think I should read these sections a bit closer now…