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Reducing subspace

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In linear algebra, a reducing subspace {\displaystyle W} of a linear map {\displaystyle T:V\to V} from a Hilbert space {\displaystyle V} to itself is an invariant subspace of {\displaystyle T} whose orthogonal complement {\displaystyle W^{\perp }} is also an invariant subspace of {\displaystyle T.} That is, {\displaystyle T(W)\subseteq W} and {\displaystyle T(W^{\perp })\subseteq W^{\perp }.} One says that the subspace {\displaystyle W} reduces the map {\displaystyle T.}

One says that a linear map is reducible if it has a nontrivial reducing subspace. Otherwise one says it is irreducible.

If {\displaystyle V} is of finite dimension {\displaystyle r} and {\displaystyle W} is a reducing subspace of the map {\displaystyle T:V\to V} represented under basis {\displaystyle B} by matrix {\displaystyle M\in \mathbb {R} ^{r\times r}} then {\displaystyle M} can be expressed as the sum

{\displaystyle M=P_{W}MP_{W}+P_{W^{\perp }}MP_{W^{\perp }}}

where {\displaystyle P_{W}\in \mathbb {R} ^{r\times r}} is the matrix of the orthogonal projection from {\displaystyle V} to {\displaystyle W} and {\displaystyle P_{W^{\perp }}=I-P_{W}} is the matrix of the projection onto {\displaystyle W^{\perp }.}[1] (Here {\displaystyle I\in \mathbb {R} ^{r\times r}} is the identity matrix.)

Furthermore, {\displaystyle V} has an orthonormal basis {\displaystyle B'} with a subset that is an orthonormal basis of {\displaystyle W}. If {\displaystyle Q\in \mathbb {R} ^{r\times r}} is the transition matrix from {\displaystyle B} to {\displaystyle B'} then with respect to {\displaystyle B'} the matrix {\displaystyle Q^{-1}MQ} representing {\displaystyle T} is a block-diagonal matrix

{\displaystyle Q^{-1}MQ=\left[{\begin{array}{cc}A&0\\0&B\end{array}}\right]}

with {\displaystyle A\in \mathbb {R} ^{d\times d},} where {\displaystyle d=\dim W}, and {\displaystyle B\in \mathbb {R} ^{(r-d)\times (r-d)}.}

References

[edit]
  1. ^ R. Dennis Cook (2018). An Introduction to Envelopes : Dimension Reduction for Efficient Estimation in Multivariate Statistics. Wiley. p. 7.


Reducing subspace
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