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The “simplex of probabilty measures” and semi-continuous compressed sensing

Posted by Dirk Lorenz under

Math,

Sparsity | Tags:

Basis pursuit,

compressed sensing,

probability measures,

sparsity,

weak* convergence |

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Let be a compact subset of and consider the space of continuous functions with the usual supremum norm. The Riesz Representation Theorem states that the dual space of is in this case the set of all *Radon measures*, denoted by and the canonical duality pairing is given by

We can equip with the usual notion of weak* convergence which read as

We call a measure positive if implies that . If a positive measure satisfies (i.e. it integrates the constant function with unit value to one), we call it a probability measure and we denote with the set of all probability measures.

**Example 1** * Every non-negative integrable function with induces a probability measure via
*

*
* Quite different probability measures are the -measures: For every there is the -measure at this point, defined by

* *

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

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

*
** we have the is the set of convex combinations of the measures (). Hence, resembles the hyper-octahedron (aka cross polytope or -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 , and suppose that the subset of consisting of the probability measures such that for
* is not empty. Then there exists such that

- and are convex combinations of at most -measures, and
- it holds that for all we have

* *

In terms of compressed sensing this says: Among all probability measures which comply with the data measured by linear measurements, there are two extremal ones which consists of -measures.

Note that something similar to “support-pursuit” does not work here: The minimization problem does not make much sense, since for all .

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