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Reducing subspace

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Concept in linear algebra

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

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

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

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

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

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

Q 1 M Q = [ A 0 0 B ] {\displaystyle Q^{-1}MQ=\left}

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

References

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


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