Fermat's Last Theorem in fiction: Difference between revisions

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The '''orthogonal Procrustes problem''' <ref>{{Citation | last1=Gower | first1=J.C | last2=Dijksterhuis | first2=G.B. | year=2004 | title=Procrustes Problems | publisher = Oxford University Press}}</ref> is a [[matrix approximation]] problem in [[linear algebra]]. In its classical form, one is given two [[Matrix (mathematics)|matrices]] <math>A</math> and <math>B</math> and asked to find an [[orthogonal matrix]] <math>R</math> which most closely maps <math>A</math> to <math>B</math>. <ref>{{Citation | last1=Hurley | first1=J.R. | last2=Cattell | first2=R.B. | year=1962 | title=Producing direct rotation to test a hypothesized factor structure | journal = Behavioral Science | volume = 7 | pages=258&ndash;262 | doi=10.1002/bs.3830070216 | issue=2}}</ref> Specifically,
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:<math>R = \arg\min_\Omega \|A\Omega-B\|_F \quad\mathrm{subject\ to}\quad \Omega^T
\Omega=I,</math>
 
where <math>\|\cdot\|_F</math> denotes the [[Frobenius norm]].
 
The name [[Procrustes]] refers to a bandit from Greek mythology who made his victims fit his bed by either stretching their limbs or cutting them off.
 
== Solution ==
This problem was originally solved by [[Peter Schonemann]] in a 1964 thesis. The individual solution was later published. <ref>{{Citation | last=Schonemann | first=P.H. | authorlink = Peter Schonemann | year=1966 | title=A generalized solution of the orthogonal Procrustes problem | journal=Psychometrika | volume=31 | pages=1–10 | url=http://www2.psych.purdue.edu/~phs/pdf/3.pdf | doi=10.1007/BF02289451 | postscript=.}}</ref> A proof is also given in <ref>{{Citation | last = Zhang | first = Z. | contribution = A Flexible New Technique for Camera Calibration | series = MSR-TR-98-71 | url=http://research.microsoft.com/en-us/um/people/zhang/Papers/TR98-71.pdf  | year = 1998}}
</ref>
 
This problem is equivalent to finding the nearest orthogonal matrix to a given matrix <math>M=A^{T}B</math>.  To find this orthogonal matrix <math>R</math>, one uses the [[singular value decomposition]]
:<math>M=U\Sigma V^*\,\!</math>
to write
:<math>R=UV^*.\,\!</math>
 
== Generalized/constrained Procrustes problems ==
There are a number of related problems to the classical orthogonal Procrustes problem.  One might generalize it by seeking the closest matrix in which the columns are [[orthogonal]], but not necessarily [[orthonormal]]. <ref>{{Citation| last=Everson| first=R| year=1997| title=Orthogonal, but not Orthonormal, Procrustes Problems| url=http://citeseer.ist.psu.edu/everson97orthogonal.html}}</ref> 
 
Alternately, one might constrain it by only allowing [[rotation matrix|rotation matrices]] (i.e. orthogonal matrices with [[determinant]] 1, also known as [[Orthogonal matrix|special orthogonal matrices]]).  In this case, one can write (using the above decomposition <math>M=U\Sigma V^*</math>)
 
:<math>R=U\Sigma'V^*,\,\!</math>
 
where <math>\Sigma'\,\!</math> is a modified <math>\Sigma\,\!</math>, with the smallest singular value replaced by <math>\operatorname{sign}(\det(UV^*))</math> (+1 or -1), and the other singular values replaced by 1, so that the determinant of R is guaranteed to be positive. <ref>{{Citation|last1=Eggert|first1=DW| last2=Lorusso|first2=A| last3=Fisher|first3=RB| title=Estimating 3-D rigid body transformations: a comparison of four major algorithms| journal=Machine Vision and Applications| volume=9| issue=5| pages=272&ndash;290| year=1997|doi=10.1007/s001380050048}}</ref> For more information, see the [[Kabsch algorithm]].  
 
== See also ==
* [[Procrustes analysis]]
* [[Procrustes transformation]]
 
== References ==
<references/>
 
[[Category:Linear algebra]]
[[Category:Matrix theory]]
[[Category:Singular value decomposition]]

Revision as of 21:06, 11 February 2014

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