Kværner-process: Difference between revisions

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In [[mathematics]], specifically in [[linear algebra]], the '''spark''' of a matrix ''A'' is the smallest number ''n'' such that there exists a set of ''n'' columns in ''A'' which are linearly dependent. Formally,
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:<math>\mathrm{spark}(A) = \min_{d \ne 0} \|d\|_0 \text{ s.t. } A d = 0. </math>
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By contrast, the [[rank (linear algebra)|rank]] of a matrix is the smallest number ''k'' such that ''all'' sets of ''k''&nbsp;+&nbsp;1 columns in ''A'' are linearly dependent.
 
The concept of the spark is of use in the theory of [[compressive sensing]], where requirements on the spark of the measurement matrix are used to ensure stability and consistency of various estimation techniques.
 
== References ==
* {{Citation
  | last = Donoho
  | first = David L.
  | author-link = David Donoho
  | last2 = Elad
  | first2 = Michael
  | author2-link =
  | title = Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ<sup>1</sup> minimization
  | journal = Proc. Nat. Acad. Sci.
  | volume = 100
  | issue = 5
  | pages = 2197–2202
  | date = March 4, 2003
  | url = http://www.pnas.org/cgi/doi/10.1073/pnas.0437847100
  | doi = 10.1073/pnas.0437847100
  | id =
  | pmid = 16576749
  | pmc = 153464 }}
 
[[Category:Signal processing]]
[[Category:Matrix theory]]
 
 
{{Linear-algebra-stub}}
{{Signal-processing-stub}}

Revision as of 22:04, 17 June 2013

In mathematics, specifically in linear algebra, the spark of a matrix A is the smallest number n such that there exists a set of n columns in A which are linearly dependent. Formally,

spark(A)=mind0d0 s.t. Ad=0.

By contrast, the rank of a matrix is the smallest number k such that all sets of k + 1 columns in A are linearly dependent.

The concept of the spark is of use in the theory of compressive sensing, where requirements on the spark of the measurement matrix are used to ensure stability and consistency of various estimation techniques.

References

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