In sparse recovery one aims to leverage the sparsity of some vector {u} to get a good reconstruction of this vector from a noisy and indirect measurement {u^{0}}. Sparsity can be expressed in two different ways: The analysis way and the synthesis way. For both of them you need a system of other vectors {v_{k}}.

  • The analysis way: Here you consider the inner products {\langle u,v_{k}\rangle} and the sparsity assumption is, that this sequence is sparse.
  • The synthesis way: Here you assume that there is sparse sequence of coefficients {\psi_{k}} such that {u=\sum_{k} \psi_{k}v_{k}}.

The first approach is called “analysis” since here one analyzes the vector {u} with the system of vectors. In the second approach, you use the vectors {v_{k}} to synthesize the vector {u}.

We will keep things very simple here and only consider a noisy observation (and not an indirect one). The recovery approach in the analysis way is then to find {u} as a solution of

\displaystyle \tfrac12\|u-u_{0}\| + \alpha R(\langle u,v_{k}\rangle)

while for the synthesis way you write {u = \sum_{k} \psi_{k}v_{k} = V\psi} (where we collected the vectors {v_{k}} as the columns of the matrix {V}) and solve

\displaystyle \tfrac12\|V\psi-u_{0}\| + \alpha R(\psi)

The functional {R} should enforce the sparsity of the vector of coefficients in the former case and the sparsity of {\psi} in the latter case. With the matrix {V} the analysis way reads as

\displaystyle \tfrac12\|u-u_{0}\| + \alpha R(V^{T}u)

One important observation is, that both approaches coincide if the matrix {V} is orthonormal: Since {V^{-1} = V^{T}} in this case, {u=V\psi} is equivalent to {\psi = V^{T}u}, i.e. {\psi_{k} = \langle u,v_{k}\rangle}. For other sets of vectors {v_{k}}, this is not true and it is not so clear, how the analysis and the synthesis approach are related. In this post I’d like to show one instance where both approaches do lead to results that are not even close to being similar.

1. The “ROF analysis” approach to denoising

If {u^{0}} is an image, the famous Rudin-Osher-Fatemi denoising model fits under the “analysis” framework: The vectors {v_{k}} are all the finite differences of neighboring pixels such that {V^{T}u = \nabla u} with the discrete gradient {\nabla u}. As sparsifying functional you take a mixed {2}{1} norm, i.e. {R(\nabla u) = \|\nabla u\|_{2,1} = \sum_{ij}\sqrt{(\partial_{1}u_{ij})^{2} + (\partial_{2}u_{ij})^{2}}}. The denoised {u} is the solution to

\displaystyle \min_{u} \tfrac12\|u-u^{0}\|^{2} + \alpha\|\nabla u\|_{2,1}

Thus, the model will lead to some denoised {u} with a sparse gradient. This sparse gradient is partly the reason for the success of the model in that it keeps edges, but is also responsible for the drawback of this approach: The denoised image will be mostly piesewise constant.

2. The “ROF synthesis” approach

As {V^{T} = \nabla}, the corresponding synthesis approach would be to solve

\displaystyle \min_{\psi}\tfrac12\|\nabla^{T}\psi - u^{0}\|^{2} + \alpha \|\psi\|_{2,1}

and the set {u = \nabla^{T}\psi}. In this approach, the denoised image will be a sparse linear combination of particular two-pixel images. If you think about that, it does not really sound like a good idea to approximate an image by a sparse linear combination of two-pixel images. (One technicality: The operator {\nabla^{T}} is not onto – {\nabla} has a non-trivial kernel, consisting of the constant images and hence, all images in the range of {\nabla^{T}} have zero mean. Thus, to get something remotely close to {u^{0}} one should subtract the mean of {u^{0}} before the reconstruction and add it back afterwards.)

Here are some results:



Note that the results actually fit to what we would have predicted: For larger {\alpha} we get a “sparser gradient” (less edges, less variation) for the analysis approach, and “sparser two-pixel combinations” for the synthesis approach. In fact, for the synthesis approach to give something that is remotely close to the image, one needs quite a lot two-pixel images, i.e. a very low sparsity.

In conclusion, one should not consider the analysis and synthesis approach to be something similar.

Incidentally, the dual of the analysis approach

\displaystyle \min_{u} \tfrac12\|u-u^{0}\|^{2} + \alpha\|\nabla u\|_{2,1}

can be written as

\displaystyle \min_{\|\phi\|_{2,\infty}\leq\alpha}\tfrac12\|\nabla^{T}\phi-u^{0}\|^{2} = \min_{\phi}\tfrac12\|\nabla^{T}\phi-u^{0}\|^{2} + I_{\|\cdot\|_{2,\infty}\leq\alpha}(\phi)

which looks related to the synthesis approach (one basically replaces the {2,1}-norm by its conjugate function). Actually, a similar relation between the dual of the analysis approach and the synthesis approach always holds.


Last week Christoph Brauer, Andreas Tillmann and myself uploaded the paper A Primal-Dual Homotopy Algorithm for {\ell_{1}}-Minimization with {\ell_{\infty}}-Constraints to the arXiv (and we missed being the first ever arXiv-paper with a non-trivial five-digit identifier by twenty-something papers…). This paper specifically deals with the optimization problem

\displaystyle \begin{array}{rcl} \min_{x}\|x\|_{1}\quad\text{s.t.}\quad \|Ax-b\|_{\infty}\leq \delta \end{array}

where {A} and {b} are a real matrix and vector, respecively, of compatible size. While the related problem with {\ell_{2}} constraint has been addressed quite often (and the penalized problem {\min_{x}\|x\|_{1} + \tfrac1{2\lambda}\|Ax-b\|_{2}^{2}} is even more popular) there is not much code around to solve this specific problem. Obvious candidates are

  • Linear optimization: The problem can be recast as a linear program: The constraint is basically linear already (rewriting it with help of the ones vector {\mathbf{1}} as {Ax\leq \delta\mathbf{1}+b}, {-Ax\leq \delta\mathbf{1}-b}) and for the objective one can, for example, perform a variable split {x = x^{+}-x^{-}}, {x^{-},x^{+}\geq 0} and then write {\|x\|_{1} = \mathbf{1}^{T}x^{+}+ \mathbf{1}^{T}x^{-}}.
  • Splitting methods: The problem is convex problem of the form {\min_{x}F(x) + G(Ax)} with

    \displaystyle \begin{array}{rcl} F(x) & = & \|x\|_{1}\\ G(y) & = & \begin{cases} 0 & \|y-b\|\leq\delta\\ \infty & \text{else.} \end{cases} \end{array}

    and hence, several methods for these kind of problem are available, such as the alternating direction method of multipliers or the Chambolle-Pock algorithm.

The formulation as linear program has the advantage that one can choose among a lot of highly sophisticated software tools and the second has the advantage that the methods are very easy to code, usually in just a few lines. Drawbacks are, that both methods do exploit too much specific structure of the problem.

Application of the problem with {\ell_{\infty}} constraint are, for example:

  • The Dantzig selector, a statistical estimation technique, were the problem is

    \displaystyle \begin{array}{rcl} \min_{x}\|x\|_{1}\quad\text{s.t.}\quad \|A^{T}(Ax-b)\|_{\infty}\leq\delta. \end{array}

  • Sparse dequantization as elaborated here by Jacques, Hammond and Fadili and applied here to de-quantizaton of speech signals by Christoph, Timo Gerkmann and myself.

We wanted to see if one of the most efficient methods known for sparse reconstruction with {\ell_{2}} penalty, namely the homotopy method, can be adapted to this case. The homotopy method for {\min_{x}\|x\|_{1} + \tfrac1{2\lambda}\|Ax-b\|_{2}^{2}} builds on the observation that the solution for {\lambda\geq \|A^{T}b\|_{\infty}} is zero and that the set of solutions {x_{\lambda}}, parameterized by the parameter {\lambda}, is piecewise linear. Hence, on can start from {\lambda_{0}= \|A^{T}b\|_{\infty}}, calculate which direction to go, how far the breakpoint is away, go there and start over. I’ve blogged on the homotopy method here already and there you’ll find some links to great software packages, but also the fact that there can be up to exponentially many breakpoints. However, in practice the homotopy method is usually blazingly fast and with some care, can be made numerically stable and accurate, see, e.g. our extensive study here (and here is the optimization online preprint).

The problem with {\ell_{\infty}} constraint seems similar, especially it is clear that for {\delta = \|b\|_{\infty}}, {x=0} is a solution. It is also not so difficult to see that there is a piecewise linear path of solutions {x_{\delta}}. What is not so clear is, how it can be computed. It turned out, that in this case the whole truth can be seen when the problem is viewed from a primal-dual viewpoint. The associated dual problem is

\displaystyle \begin{array}{rcl} \max_{y}\ -b^{T}y - \delta\|y\|_{1}\quad\text{s.t.}\quad\|A^{T}y\|_{\infty}\leq\infty \end{array}

and a pair {(x^{*},y^{*})} is primal and dual optimal if and only if

\displaystyle \begin{array}{rcl} -A^{T}y^{*}&\in&\text{Sign}(x^{*})\\ Ax^{*}-b & \in & \delta\text{Sign}(y^{*}) \end{array}

(where {\text{Sign}} denotes the sign function, multivalued at zero, giving {[-1,1]} there). One can note some things from the primal-dual optimality system:

  • You may find a direction {d} such that {(x^{*}+td,y^{*})} stays primal-dual optimal for the constraint {\leq\delta-t} for small {t},
  • for a fixed primal optimal {x_{\delta}} there may be several dual optimal {y_{\delta}}.

On the other hand, it is not that clear which of the probably many dual optimal {y_{\delta}} allows to find a new direction {d} such that {x_{\delta}+td} with stay primal optimal when reducing {\delta}. In fact, it turned out that, at a breakpoint, a new dual variable needs to be found to allow for the next jump in the primal variable. So, the solution path is piecewise linear in the primal variable, but piecewise constant in the dual variable (a situation similar to the adaptive inverse scale space method).

What we found is, that some adapted theorem of the alternative (a.k.a. Farkas’ Lemma) allows to calculate the next dual optimal {y} such that a jump in {x} will be possible.

What is more, is that the calculation of a new primal or dual optimal point amounts to solving a linear program (in contrast to a linear system for {\ell_{2}} homotopy). Hence, the trick of up- and downdating a suitable factorization of a suitable matrix to speed up computation does not work. However, one can somehow leverage the special structure of the problem and use a tailored active set method to progress through the path. Our numerical tests indicated is that the resulting method, which we termed {\ell_{1}}-Houdini, is able to solve moderately large problems faster than a commercial LP-solver (while also not only solving the given problem, but calculating the whole solution path on the fly) as can be seen from this table from the paper:


The code of \ell_1-Houdini is on Christoph’s homepage, you may also reproduce the data in the above table with your own hardware.

Yesterday I uploaded the paper “Linear convergence of the Randomized Sparse Kaczmarz Method” by Frank Schöpfer and myself to the arXiv.

Recall that the Kaczmarz method for linear systems

\displaystyle \begin{array}{rcl} Ax&=&b \end{array}


\displaystyle \begin{array}{rcl} x^{k+1} &=& x^{k} - \frac{\langle a_{i},x_{k}\rangle-b_{i}}{\|a_{i}\|^{2}}a_{i} \end{array}

where {a_{i}} is the {i}-th row of {A}, {b_{i}} is the {i}th entry of {b} and the index {i} is chosen according to some rule. We could, e.g. choose the rows in a cyclic manner, i.e. starting with the first row, proceeding to the next row and once we came to the last row we would start over from the first again. It is known (and probably proved by Kaczmarz himself) that the method converges to a solution of {Ax=b} whenever the system has a solution. Moreover, it easy to see that we converge to the minimum norm solution in case of underdetermined systems when the method is initialized with zero. This is due to the fact that the whole iteration takes place in the range space of {A^{T}}.

In this and this paper we proposed a simple modification of the Kaczmarz method, that makes it converge to sparse solutions. The modification is simply

\displaystyle \begin{array}{rcl} z^{k+1} & = & z^{k} - \frac{\langle a_{i},x_{k}\rangle-b_{i}}{\|a_{i}\|^{2}}a_{i}\\ x^{k+1}& = & S_{\lambda}(z^{k+1}) \end{array}

where {S_{\lambda}(x) = \max(|x|-\lambda,0)\text{sign}(x)} is the soft thresholding function. In this paper we proved that this variant converges, when initialized with zero and for a consistent system, to the solution of

\displaystyle \begin{array}{rcl} \min_{x}\lambda\|x\|_{1} + \tfrac12\|x\|_{2}^{2},\quad\text{s.t.}\quad Ax=b. \end{array}

For not too small values of {\lambda} this is indeed a sparse solution of {Ax=b} and Frank also proved that there is a threshold such that for {\lambda} larger than this threshold the solution is also the minimum {\ell^{1}} solution.

In general, convergence rates for the Kaczmarz method (and its sparse variant) are hard to prove. To convince oneself of this fact note that the convergence speed can drastically change if the rows of the system are reordered. The situation changes if one uses a randomization of the sparse Kaczmarz method where, in each iteration, a row is chose at random. Strohmer and Vershynin proved that this randomization leads to linear convergence rate. In the above mentioned paper we were able to prove the same result for the randomized sparse Kaczmarz method. While this sounds like an obvious generalization, the methods we use are totally different. While the linear convergence of the randomized Kaczmarz method can be proven in a few lines(well, probably one page) using only very basic tools, we need, among other things, quite intricate error bounds for Bregman projections w.r.t. piecewise linear-quadratic convex functions.

In fact, the linear convergence can be observed in practice and we illustrate the usefulness of the randomization and also the “sparsification” on some examples in the paper. For example the following figure shows the decay of the residual for the the randomized Kaczmarz method (black), the randomized sparse Kaczmarz method (red) and the randomized sparse Kaczmarz method with exact steps (green) which I did not explain here.


More details are in the paper…

With this post I delve into a topic which is somehow new to me, although I planned to look deeper into this for quite some time already. I stumbled upon the paper Gromov-Wasserstein distances and the metric approach to object matching by Facundo Mémoli which was a pleasure to read and motivated this post.

1. Comparing measures with norms and metrics

There are different notions in mathematics to compare two objects, think of the size of real numbers, the cardinality of sets or the length of the difference of two vectors. Here we will deal with not only comparison of objects but with “measures of similarity”. Two fundamental notions for this are norms in vector spaces and metrics. The norm is the stronger concept in that it uses more structure than a metric and also, every norm induces a metric but not the other way round. There are occasions in which both a norm and a metric are available but lead to different concepts of similarity. One of these instances occurs in sparse recovery, especially in the continuous formulation, e.g. as described in a previous post. Consider the unit interval {I = [0,1]} and two Radon measures {\mu_1} and {\mu_2} on {I} ({I} could also be an aritrary metric space). On the space of Radon measures {\mathfrak{M}(I)} there is the variation norm

\displaystyle \|\mu\|_{\mathfrak{M}}= \sup_\Pi\sum_{A\in\Pi}|\mu(A)|

where the supremum is taken over all partitions {\Pi} of {I} into a finite number of measurable sets. Moreover, there are different metrics one can put on the space of Radon measures, e.g. the Prokhorov metric which is defined for two probability measures (e.g. non-negative ones with unit total mass)

\displaystyle  \begin{array}{rcl}  d_P(\mu_1,\mu_2) & = & \inf\{\epsilon>0\ :\ \mu_1(A)\leq \mu_2(A^\epsilon) + \epsilon,\nonumber\\ & & \qquad \mu_2(A)\leq \mu_1(A^\epsilon) + \epsilon\ \text{for all measurable}\ A\} \end{array}

where {A^\epsilon} denotes the {\epsilon}-neighborhood of {A}. Another familiy of metrics are the Wasserstein metrics: For {p\geq 1} define

\displaystyle d_{W,p}(\mu_1,\mu_2) = \Big(\inf_\nu\int_{I\times I} |x-y|^p d\nu(x,y)\Big)^{1/p} \ \ \ \ \ (1)

where the infimum is taken over all measure couplings of {\mu_1} and {\mu_2}, that is, all measures {\nu} on {I\times I} such that for measurable {A} it holds that

\displaystyle \nu(A\times I) = \mu_1(A)\ \text{and}\ \nu(I\times A) = \mu_2(A).

Example 1 We compare two Dirac measures {\mu_1 = \delta_{x_1}} and {\mu_2 = \delta_{x_2}} located at distinct points {x_1\neq x_2} in {I} as seen here:

058_two_diracsThe variation norm measures their distance as

\displaystyle \|\mu_1-\mu_2\|_{\mathfrak{M}} = \sup_\Pi\sum_{A\in\Pi}|\delta_{x_1}(A) - \delta_{x_2}(A)| = 2

(choose {\Pi} such that it contains {A_1} and {A_2} small enough that {x_1\in A_1}, {x_2\in A_2} but {x_1\notin A_2} and {x_2\notin A_1}). The calculate the Prokhorov metric note that you only need to consider {A}‘s which contain only one of the points {x_{1/2}} and hence, it evaluates to

\displaystyle d_P(\mu_1,\mu_2) = |x_1-x_2|.

For the Wasserstein metric we observe that there is only one possible measure coupling of {\delta_{x_1}} and {\delta_{x_2}}, namely the measure {\nu = \delta_{(x_1,x_2)}}. Hence, we have

\displaystyle d_{W,p}(\mu_1,\mu_2) = \Big(\int_{I\times I}|x-y|^pd\delta_{(x_1,x_2)}(x,y)\Big)^{1/p} = |x_1-x_2|.

The variation norm distinguishes the two Diracs but is not able to grasp the distance of their supports. On the other hand, both metrics return the geometric distance of the supports in the underlying space {I} as distance of the Diracs. Put in pictures: The variation norm of the difference measures the size ob this object


while both metrics capture the distance of the measures like here

Two diracs

It should not stay unnoted that convergence in both the Prokhorov metric and the Wasserstein metrics is exactly the weak convergence of probability measures.

The above example provides a motivation to study metric structures on spaces, even if they are also equipped with a norm. Another reason to shift attention from normed spaces to metric spaces is the fact that there has emerged a body of work to build a theory of analysis in metric spaces (see, e.g. this answer on mathoverflow or the book Gradient Flows: In Metric Spaces And In The Space Of Probability Measures by Ambrosio, Gigli and Savaré (which puts special emphasis on the space of probability measures)). Yet another motivation for the study of metrics in this way is the problem of comparing shapes (without being precisely defined yet): Which of these shapes look most alike?


(Note that shapes need not to be two dimensional figures, you may also think of more complex objects like surfaces in three dimensions or Riemannian manifolds.)

One may also ask the question how two compare different images defined on different shapes, i.e. different “distributions of colour” on two different shapes.

2. Comparing shapes: Metric spaces

Up to now we tried to compare different measures, defined on the same set. At least to me it seems that both the Prokhorov and the Wasserstein metrics are suited to measure the similarity of measures and in fact, they do so somehow finer than the usual norm does.

Let’s try to go one step further and ask ourselves, how we could compare two measures {\mu_1} and {\mu_2} which are defined on two different sets? While thinking about an answer one need to balance several things:

  • The setup should be general enough to allow for the comparison of a wide range of objects.
  • It should include enough structure to allow meaningful statements.
  • It should lead to a measure which is easy enough to handle both analytically and computationally.

For the first and second bullet: We are going to work with measures not on arbitrary sets but on metric spaces. This will allow to measure distances between points in the sets and, as you probably know, does not pose a severe restriction. Although metric spaces are much more specific than topological spaces, we still aim at quantitative measures which are not provided by topologies. With respect to the last bullet: Note that both the Prokhorov and the Wasserstein metric are defined as infimums over fairly large and not too well structured sets (for the Prokhorov metric and need to consider all measurable sets and their {\epsilon}-neighborhoods, for the Wasserstein metric, one need to consider all measure couplings). While they can be handled quite well theoretically, their computational realization can be cumbersome.

In a similar spirit than Facundo Memoli’s paper we work our way up from comparing subsets of metric spaces up to comparing two different metric spaces with two measures defined on them.

2.1. Comparing compact subsets of a metric space: Hausdorff

Let {(X,d)} be a compact metric space. Almost hundred years ago Hausdorff introduced a metric on the family of all non-empty compact subsets of a metric space as follows: The Hausdorff metric of two compact subsets {A} and {B} of {X} is defined as

\displaystyle d_H(A,B) = \inf\{\epsilon>0 \ :\ A\subset B_\epsilon,\ B \subset A_\epsilon\}

(again, using the notion of {\epsilon}-neighborhood). This definition seems to be much in the spirit of the Prokhorov metric.

Proposition 2.1 in Facundo Memolis paper shows that the Hausdorff metric has an equivalent description as

\displaystyle d_H(A,B) = \inf_R \sup_{(a,b) \in R} d(a,b)

where the infimum is taken over all correspondences {R} of {A} and {B}, i.e., all subset {R\subset A\times B} such that for all {a\in A} there is {b\in B} such that {(a,b) \in R} and for all {b\in B} there {a\in A} such that {(a,b)\in R}. One may also say set coupling of {A} and {B} instead of correspondence.

Example 2 There is always the full coupling {R = A\times B}. Three different set couplings of two subsets {A} and {B} of the unit interval are shown here:


the “full one” {A\times B} in green and two “slim” ones in red and orange. Other “slim” couplings can be obtained from surjective mappings {f:A\rightarrow B} by {R = \{(a,f(a))\ :\ a\in A\}} (or with the roles of {A} and {B} swapped): If you couple a set {A} with itself, there is also the trivial coupling

\displaystyle R = \{(a,a)\ : \ a\in A\}

which is just the diagonal of {A\times A}

Note that the alternative definition of the Hausdorff metric is more in the spirit of the Wasserstein metric: It does not use enlarged objects (by {\epsilon}-neighborhoods) but couplings.

The Hausdorff metric is indeed a metric on the set {\mathfrak{C}(X)} of all non-empty compact subsets of a metric space {X} and if {X} itself is compact it even holds that {(\mathfrak{C}(X),d_H)} is a compact metric space (a result, known as Blaschke Selection Theorem).

One may say that we went up an abstraction ladder one step by moving from {(X,d)} to {(\mathfrak{C}(X),d_H)}.

2.2. Comparing compact metric spaces: Gromov-Hausdorff

In the previous subsection we worked within one metric space {X}. In the book “Metric Structures for Riemannian and Non-Riemannian Spaces” Misha Gromov introduced a notion to compare two different metric spaces. For compact metric space {X} and {Y} the Gromov-Hausdorff metric is defined as

\displaystyle d_{GH}(X,Y) = \inf_{Z,f,g} d_H(f(X),g(Y)) \ \ \ \ \ (2)

where the infimum is taken over

  • all metric spaces {Z} and
  • all isometric embeddings {f} and {g} which embed {X} and {Y} into {Z} respectively.

In words: To compute the Gromov-Hausdorff metric, you try embed both {X} and {Y} into a common larger space isometrically such that they are as close as possible according to the Hausdorff metric in that space.

Strictly speaking, the above definition is not well stated as one can not form an infimum over all metric spaces since this collection does not form a set according to the rules of set theory. More precisely one should write that {d_{GH}(X,Y)} is the infimum over all {r>0} such that there exists a metric space {Z} and isometric embeddings {f} and {g} of {X} and {Y}, respectively, such that {d_H(f(X),g(Y))<r}.

As the Hausdorff metric could be reformulated with set couplings there is a reformulation of the Gromov-Hausdorff metric based on metric couplings: A metric coupling of two metric spaces {(X,d_X)} and {(Y,d_Y)} is a metric {d} on the disjoint union {X\sqcup Y} of {X} and {Y} such that for all {x,x'\in X} and {y,y'\in Y} it holds that {d(x,x') = d_X(x,x')} and {d(y,y') = d_Y(y,y')}.

Example 3 We couple a metric space {(X,d)} with itself. We denote with {(X',d')} an identical copy of {(X,d)} and look for a metric {D} on {X\times X'} that respects the metrics {d} and {d'} in the way a metric coupling has to.

To distinguish elements from {X} and {X'} we put a {'} on all quantities from {X'}. Moreover, for {x\in X} we denote by {x'} its identical copy in {X'} (and similarly for {x'\in X'}, {x} is its identical twin). Then, for any {\epsilon>0} we can define {D_\epsilon(x,x') = D_\epsilon(x',x) = \epsilon} (i.e. the distance between any two identical twins is {\epsilon}. By the triangle inequality we get for {x\in X} and {y'\in X'} that {D_\epsilon(x,y')} should fulfill

\displaystyle D_\epsilon(x',y') - D_\epsilon(x',x) \leq D_\epsilon(x,y') \leq D_\epsilon(x,y) + D_\epsilon(y,y')

and hence

\displaystyle d(x,y) - \epsilon \leq D_\epsilon(x,y') \leq d(x,y) + \epsilon.

Indeed we can choose {D_\epsilon(x,y') = d(x,y)} if {x\in X} and {y'\in Y} leading to one specific metric coupling for any {\epsilon}. This couplings allow to distinguish identical twins and behave as a metric on the whole disjoint union. In the limiting case {\epsilon\rightarrow 0} we do not obtain a metric but a semi-metric or pseudo-metric which is just the same as a metric but without the assumption that {d(x,y) = 0} implies that {x=y}.

Example 4 The above example of a metric coupling of a metric space with itself was somehow “reproducing” the given metric as accurate as possible. There are also other couplings that put very different distances to points {D(x,y')} and there is also a way to visualize metric couplings: When building the disjoint union of two metric spaces {X} and {Y}, you can imagine this as isometrically embedding both in a larger metric space {Z} in a non-overlapping way and obtain the metric coupling {D} as the restriction of the metric on {Z} to {X\sqcup Y}. For {X=Y=[0,1]} you can embed both into {Z = {\mathbb R}^2}. A metric coupling which is similar (but not equal) to the coupling of the previous example is obtained by putting {X} and {Y} side by side at distance {\epsilon} as here (one space in green, the other in blue).


A quite different coupling is obtained by putting {X} and {Y} side by side, but in a reversed way as here:


You may even embed them in a more weired way as here:


but remember that the embeddings has to be isometric, hence, distortions like here are not allowed.

058_metric_coupling_embedding4This example illustrate that the idea of metric coupling is in similar spirit as of “embedding two spaces in a common larger one”.

With the notion of metric coupling, the Gromov-Hausdorff metric can be written as

\displaystyle d_{GH}(X,Y) = \inf_{R,d} \sup_{(x,y)\in R} d(x,y) \ \ \ \ \ (3)

where the infimum is taken over all set couplings {R} of {X} and {Y} and all metric couplings {d} of {(X,d_X)} and {(Y,d_Y)}.

In words: To compute the Gromov-Hausdorff metric this way, you look for a set coupling of the base sets {X} and {Y} and a metric coupling {d} of the metrics {d_X} and {d_Y} such that the maximal distance of two coupled points {x} and {y} is as small as possible. While this may look more complicated than the original definition from~(2), note that the original definition uses all metric spaces {Z} in which you can embed {X} and {Y} isometrically, which seems barely impossible to realize. Granted, the new definition also considers a lot of quantities.

Also note that this definition is in spirit of the Wasserstein metric from~(1): If there were natural measures {\mu_R} on the set couplings {R} we could write \begin{equation*} d_{GH}(X,Y) = \inf_{R,d} \Big(\int d(x,y)^pd\mu_R\Big)^{1/p} \end{equation*} and in the limit {p\rightarrow\infty} we would recover definition~(3).

Example 5 The Gromov-Hausdorff distance of a metric space {(X,d_X)} to itself is easily seen to be zero: Consider the trivial coupling {R = \{(x,x)\ :\ x\in X\}} from Example~2 and the family {D_\epsilon} of metric couplings from Example~3. Then we have {d_{GH}(X,X) \leq \epsilon} for any {\epsilon >0} showing {d_{GH}(X,X) = 0}. Let’s take one of the next-complicated examples and compute the distance of {X = [0,1]} and {Y=[0,2]}, both equipped with the euclidean metric. We couple the sets {X} and {Y} by {R = \{(x,2x)\ : \ x\in X\}} and the respective metrics by embedding {X} and {Y} into {{\mathbb R}^2} as follows: Put {Y} at the line from {(0,0)} to {(2,0)} and {X} at the line from {(\tfrac12,\epsilon)} to {(1\tfrac12,\epsilon)}:


This shows that {d_{GH}(X,Y) \leq \tfrac12} and actually, we have equality here.

There is another reformulation of the Gromov-Hausdorff metric, the equivalence of which is shown in Theorem 7.3.25 in the book “A Course in Metric Geometry” by Dmitri Burago, Yuri Burago and Sergei Ivanov:

\displaystyle d_{GH}(X,Y) = \tfrac12\inf_R \sup_{\overset{\overset{x_{1/2}\in X}{y_{1/2}\in Y}}{(x_i,y_i)\in R}}\big| d_X(x_1,x_2) - d_Y(y_1,y_2)\big| \ \ \ \ \ (4)

where the infimum is taken over all set couplings {R} of {X} and {Y}.

In words: Look for a set coupling such that any two coupled pairs {(x_1,y_1)} and {(x_2,y_2)} have the “most equal” distance.

This reformulation may have the advantage over the form (3) in that is only considers the set couplings and the given metrics {d_X} and {d_Y} and no metric coupling is needed.

Note that, as the previous reformulation~(3), it is also in the spirit of the Wasserstein metric: If there were natural measures {\mu_R} in the set couplings {R}, we could write

\displaystyle d_{GH}(X,Y) = \tfrac12\inf_R \Big(\int_{R\times R}\big| d_X(x_1,x_2) - d_Y(y_1,y_2)\big|^p d\mu_R(x_1,y_1)d\mu_R(x_2,y_2)\Big)^{1/p}.

and recover the formulation~(4) in the limit {p\rightarrow\infty}.

One may say that we went up an abstraction ladder one step further by moving from {(X,d)} to {(\mathfrak{C}(X),d_H)} to {(\text{All compact metric spaces},d_{GH})}.

Since this post has been grown pretty long already, I decided to do the next step (which is the already announced metric on metric spaces which additionally carry some measure on them – so-called metric measure spaces) in a later post.

Let {\Omega} be a compact subset of {{\mathbb R}^d} and consider the space {C(\Omega)} of continuous functions {f:\Omega\rightarrow {\mathbb R}} with the usual supremum norm. The Riesz Representation Theorem states that the dual space of {C(\Omega)} is in this case the set of all Radon measures, denoted by {\mathfrak{M}(\Omega)} and the canonical duality pairing is given by

\displaystyle  \langle\mu,f\rangle = \mu(f) = \int_\Omega fd\mu.

We can equip {\mathfrak{M}(\Omega)} with the usual notion of weak* convergence which read as

\displaystyle  \mu_n\rightharpoonup^* \mu\ \iff\ \text{for every}\ f:\ \mu_n(f)\rightarrow\mu(f).

We call a measure {\mu} positive if {f\geq 0} implies that {\mu(f)\geq 0}. If a positive measure satisfies {\mu(1)=1} (i.e. it integrates the constant function with unit value to one), we call it a probability measure and we denote with {\Delta\subset \mathfrak{M}(\Omega)} the set of all probability measures.

Example 1 Every non-negative integrable function {\phi:\Omega\rightarrow{\mathbb R}} with {\int_\Omega \phi(x)dx} induces a probability measure via

\displaystyle  f\mapsto \int_\Omega f(x)\phi(x)dx.

Quite different probability measures are the {\delta}-measures: For every {x\in\Omega} there is the {\delta}-measure at this point, defined by

\displaystyle  \delta_x(f) = f(x).

In some sense, the set {\Delta} of probability measure is the generalization of the standard simplex in {{\mathbb R}^n} to infinite dimensions (in fact uncountably many dimensions): The {\delta}-measures are the extreme points of {\Delta} and since the set {\Delta} is compact in the weak* topology, the Krein-Milman Theorem states that {\Delta} is the weak*-closure of the set of convex combinations of the {\delta}-measures – similarly as the standard simplex in {{\mathbb R}^n} is the convex combination of the canonical basis vectors of {{\mathbb R}^n}.

Remark 1 If we drop the positivity assumption and form the set

\displaystyle  O = \{\mu\in\mathfrak{M}(\Omega)\ :\ |f|\leq 1\implies |\mu(f)|\leq 1\}

we have the {O} is the set of convex combinations of the measures {\pm\delta_x} ({x\in\Omega}). Hence, {O} resembles the hyper-octahedron (aka cross polytope or {\ell^1}-ball).

I’ve taken the above (with almost similar notation) from the book “ A Course in Convexity” by Alexander Barvinok. I was curious to find (in Chapter III, Section 9) something which reads as a nice glimpse on semi-continuous compressed sensing: Proposition 9.4 reads as follows

Proposition 1 Let {g,f_1,\dots,f_m\in C(\Omega)}, {b\in{\mathbb R}^m} and suppose that the subset {B} of {\Delta} consisting of the probability measures {\mu} such that for {i=1,\dots,m}

\displaystyle  \int f_id\mu = b_i

is not empty. Then there exists {\mu^+,\mu^-\in B} such that

  1. {\mu^+} and {\mu^-} are convex combinations of at most {m+1} {\delta}-measures, and
  2. it holds that for all {\mu\in B} we have

    \displaystyle  \mu^-(g)\leq \mu(g)\leq \mu^+(g).

In terms of compressed sensing this says: Among all probability measures which comply with the data {b} measured by {m} linear measurements, there are two extremal ones which consists of {m+1} {\delta}-measures.

Note that something similar to “support-pursuit” does not work here: The minimization problem {\min_{\mu\in B, \mu(f_i)=b_i}\|\mu\|_{\mathfrak{M}}} does not make much sense, since {\|\mu\|_{\mathfrak{M}}=1} for all {\mu\in B}.

ISMP LogoISMP is over now and I’m already home. I do not have many things to report on from the last day. This is not due the lower quality of the talks but due to the fact that I was a little bit exhausted, as usual at the end of a five-day conference. However, I collect a few things for the record:

  • In the morning I visited the semi-planary by Xiaojun Chenon non-convex and non-smooth minimization with smoothing methods. Not surprisingly, she treated the problem

    \displaystyle \min_x f(x) + \|x\|_p^p

    with convex and smooth {f:{\mathbb R}^n\rightarrow{\mathbb R}} and {0<p<1}. She proposed and analyzed smoothing methods, that is, to smooth the problem a bit to obtain a Lipschitz-continuous objective function {\phi_\epsilon}, minimizing this and then gradually decreasing {\epsilon}. This works, as she showed. If I remember correctly, she also treated “iteratively reweighted least squares” as I described in my previous post. Unfortunately, she did not include the generalized forward-backward methods based on {\text{prox}}-functions for non-convex functions. Kristian and I pursued this approach in our paper Minimization of non-smooth, non-convex functionals by iterative thresholding and some special features of our analysis include:

    • A condition which excludes some (but not all) local minimizers from being global.
    • An algorithm which avoids this non-global minimizers by carefully adjusting the steplength of the method.
    • A result that the number of local minimizers is still finite, even if the problem is posed in {\ell^2({\mathbb N})} and not in {{\mathbb R}^n}.

    Most of our results hold true, if the {p}-quasi-norm is replaced by functions of the form

    \displaystyle \sum_n \phi_n(|x_n|)

    with special non-convex {\phi}, namely fulfilling a list of assumptions like

    • {\phi'(x) \rightarrow \infty} for {x\rightarrow 0} (infinite slope at {0}) and {\phi(x)\rightarrow\infty} for {x\rightarrow\infty} (mild coercivity),
    • {\phi'} strictly convex on {]0,\infty[} and {\phi'(x)/x\rightarrow 0} for {x\rightarrow\infty},
    • for each {b>0} there is {a>0} such that for {x<b} it holds that {\phi(x)>ax^2}, and
    • local integrability of some section of {\partial\phi'(x) x}.

    As one easily sees, {p}-quasi-norms fulfill the assumptions and some other interesting functions as well (e.g. some with very steep slope at {0} like {x\mapsto \log(x^{1/3}+1)}).

  • Jorge Nocedalgave a talk on second-order methods for non-smooth problems and his main example was a functional like

    \displaystyle \min_x f(x) + \|x\|_1

    with a convex and smooth {f}, but different from Xiaojun Chen, he only considered the {1}-norm. His talked is among the best planary talks I have ever attended and it was a great pleasure to listen to him. He carefully explained things and put them in perspective. In the case he skipped slides, he made me feel that I either did not miss an important thing, or understood them even though he didn’t show them He argued that it is not necessarily more expensive to use second order information in contrast to first order methods. Indeed, the {1}-norm can be used to reduce the number of degrees of freedom for a second order step. What was pretty interesting is, that he advocated semismooth Newton methods for this problem. Roland and I pursued this approach some time ago in our paper A Semismooth Newton Method for Tikhonov Functionals with Sparsity Constraints and, if I remember correctly (my notes are not complete at this point), his family of methods included our ssn-method. The method Roland and I proposed worked amazingly well in the cases in which it converged but the method suffered from non-global convergence. We had some preliminary ideas for globalization, which we could not tune enough to retain the speed of the method, and abandoned the topic. Now, that the topic will most probably be revived by the community, I am looking forward to fresh ideas here.

Today I report on two things I came across here at ISMP:

  • The first is a talk by Russell Luke on Constraint qualifications for nonconvex feasibility problems. Luke treated the NP-hard problem of sparsest solutions of linear systems. In fact he did not tackle this problem but the problem to find an {s}-sparse solution of an {m\times n} system of equations. He formulated this as a feasibility-problem (well, Heinz Bauschke was a collaborator) as follows: With the usual malpractice let us denote by {\|x\|_0} the number of non-zero entries of {x\in{\mathbb R}^n}. Then the problem of finding an {s}-sparse solution to {Ax=b} is:

    \displaystyle  \text{Find}\ x\ \text{in}\ \{\|x\|_0\leq s\}\cap\{Ax=b\}.

    In other words: find a feasible point, i.e. a point which lies in the intersection of the two sets. Well, most often feasibility problems involve convex sets but here, the first one given by this “{0}-norm” is definitely not convex. One of the simplest algorithms for the convex feasibility problem is to alternatingly project onto both sets. This algorithm dates back to von Neumann and has been analyzed in great detail. To make this method work for non-convex sets one only needs to know how to project onto both sets. For the case of the equality constraint {Ax=b} one can use numerical linear algebra to obtain the projection. The non-convex constraint on the number of non-zero entries is in fact even easier: For {x\in{\mathbb R}^n} the projection onto {\{\|x\|_0\leq s\}} consists of just keeping the {s} largest entries of {x} while setting the others to zero (known as the “best {s}-term approximation”). However, the theory breaks down in the case of non-convex sets. Russell treated problem in several papers (have a look at his publication page) and in the talk he focused on the problem of constraint qualification, i.e. what kind of regularity has to be imposed on the intersection of the two sets. He could shows that (local) linear convergence of the algorithm (which is observed numerically) can indeed be justified theoretically. One point which is still open is the phenomenon that the method seems to be convergent regardless of the initialization and that (even more surprisingly) that the limit point seems to be independent of the starting point (and also seems to be robust with respect to overestimating the sparsity {s}). I wondered if his results are robust with respect to inexact projections. For larger problems the projection onto the equality constraint {Ax=b} are computationally expensive. For example it would be interesting to see what happens if one approximates the projection with a truncated CG-iteration as Andreas, Marc and I did in our paper on subgradient methods for Basis Pursuit.

  • Joel Tropp reported on his paper Sharp recovery bounds for convex deconvolution, with applications together with Michael McCoy. However, in his title he used demixing instead of deconvolution (which, I think, is more appropriate and leads to less confusion). With “demixing” they mean the following: Suppose you have two signals {x_0} and {y_0} of which you observe only the superposition of {x_0} and a unitarily transformed {y_0}, i.e. for a unitary matrix {U} you observe

    \displaystyle  z_0 = x_0 + Uy_0.

    Of course, without further assumptions there is no way to recover {x_0} and {y_0} from the knowledge of {z_0} and {U}. As one motivation he used the assumption that both {x_0} and {y_0} are sparse. After the big bang of compressed sensing nobody wonders that one turns to convex optimization with {\ell^1}-norms in the following manner:

    \displaystyle   \min_{x,y} \|x\|_1 + \lambda\|y\|_1 \ \text{such that}\ x + Uy = z_0. \ \ \ \ \ (1)

    This looks a lot like sparse approximation: Eliminating {x} one obtains the unconstraint problem \begin{equation*} \min_y \|z_0-Uy\|_1 + \lambda \|y\|_1. \end{equation*}

    Phrased differently, this problem aims at finding an approximate sparse solution of {Uy=z_0} such that the residual (could also say “noise”) {z_0-Uy=x} is also sparse. This differs from the common Basis Pursuit Denoising (BPDN) by the structure function for the residual (which is the squared {2}-norm). This is due to the fact that in BPDN one usually assumes Gaussian noise which naturally lead to the squared {2}-norm. Well, one man’s noise is the other man’s signal, as we see here. Tropp and McCoy obtained very sharp thresholds on the sparsity of {x_0} and {y_0} which allow for exact recovery of both of them by solving (1). One thing which makes their analysis simpler is the following reformulation: The treated the related problem \begin{equation*} \min_{x,y} \|x\|_1 \text{such that} \|y\|_1\leq\alpha, x+Uy=z_0 \end{equation*} (which I would call this the Ivanov version of the Tikhonov-problem (1)). This allows for precise exploitation of prior knowledge by assuming that the number {\alpha_0 = \|y_0\|_1} is known.

    First I wondered if this reformulation was responsible for their unusual sharp results (sharper the results for exact recovery by BDPN), but I think it’s not. I think this is due to the fact that they have this strong assumption on the “residual”, namely that it is sparse. This can be formulated with the help of {1}-norm (which is “non-smooth”) in contrast to the smooth {2}-norm which is what one gets as prior for Gaussian noise. Moreover, McCoy and Tropp generalized their result to the case in which the structure of {x_0} and {y_0} is formulated by two functionals {f} and {g}, respectively. Assuming a kind of non-smoothness of {f} and {g} the obtain the same kind of results and especially matrix decomposition problems are covered.