Invariant subspace

In mathematics, an invariant subspace of a linear mapping

:"T" : "V" → "V"

from some vector space "V" to itself is a subspace "W" of "V" such that "T"("W") is contained in "W". An invariant subspace of "T" is also said to be "T" invariant.

If "W" is "T"-invariant, we can restrict "T" to "W" to arrive at a new linear mapping

:"T"|"W" : "W" → "W".

Next we give a few immediate examples of invariant subspaces.

Certainly "V" itself, and the subspace {0}, are trivially invariant subspaces for every linear operator "T" : "V" → "V". For certain linear operators there is no "non-trivial" invariant subspace; consider for instance a rotation of a two-dimensional real vector space.

Let "v" be an eigenvector of "T", i.e. T"v" = λ"v". Then "W" = span {"v"} is "T" invariant. As a consequence of the fundamental theorem of algebra, every linear operator on a complex finite-dimensional vector space with dimension at least 2 has an eigenvector. Therefore every such linear operator has a non-trivial invariant subspace. The fact that the complex numbers are algebraically closed is required here. Comparing with the previous example, one can see that the invariant subspaces of a linear transformation is dependent upon the underlying scalar field of "V".

An invariant vector (fixed point of "T"), other than 0, spans an invariant subspace of dimension 1. An invariant subspace of dimension 1 will be acted on by "T" by a scalar, and consists of invariant vectors if and only if that scalar is 1.

As the above examples indicate, the invariant subspaces of a given linear transformation "T" shed light on the structure of "T". When "V" is a finite dimensional vector space over an algebraically closed field, linear transformations acting on "V" is characterized (up to similarity) by the Jordan canonical form, which decomposes "V" into invariant subspaces of "T". Many fundamental questions regarding "T" can be translated to questions about invariant subspaces of "T".

More generally, invariant subspaces are defined for sets of operators as subspaces invariant for each operator in the set. Let "L"("V") denote the algebra of linear transformations on "V", and Lat("T") be the family of subspaces invariant under "T" ∈ "L"("V"). (The "Lat" notation refers to the fact that Lat("T") forms a lattice; see discussion below.) Give a nonempty set Σ ⊂ "L"("V"), one considers the invariant subspaces invariant under each "T" ∈ Σ. In symbols,

:

For instance, it is clear that if Σ = "L"("V"), then Lat(Σ) = { {0}, "V"}.

Given a representation of a group "G" on a vector space "V", we have a linear transformation "T"("g") : "V" → "V" for every element "g" of "G". If a subspace "W" of "V" is invariant with respect to all these transformations, then it is a subrepresentation and the group "G" acts on "W" in a natural way.

As another example, let "T" ∈ "L"("V") and Σ be the algebra generated by {1, "T"}, where 1 is the identity operator. Then Lat("T") = Lat(Σ). Because "T" lies in Σ trivially, Lat(Σ) ⊂ Lat("T"). On the other hand, Σ consists of polynomials in 1 and "T", therefore the reverse inclusion holds as well.

Matrix representation

Over a finite dimensional vector space every linear transformation "T" : "V" → "V" can be represented by a matrix once a basis of "V" has been chosen.

Suppose now "W" is a "T" invariant subspace. Pick a basis "C" = {"v"₁, ..., "v"_"k"} of "W" and complete it to a basis "B" of "V". Then, with respect to this basis, the matrix representation of "T" takes the form:

:

where the upper-left block "T"₁₁ is the restriction of "T" to "W".

In other words, given an invariant subspace "W" of "T", "V" can be decomposed into the direct sum

:$V\; =\; W\; oplus\; W\text{'}.$

Viewing "T" as an operator matrix

:

it is clear that "T"₂₁: "W" → "W' " must be zero.

Determining whether a given subspace "W" is invariant under "T" is ostensibly a problem of geometric nature. Matrix representation allows one to phrase this problem algebraically. The projection operator "P" onto "W", is defined by"P"("w + w' ") = "w", where "w" ∈ "W" and "w' " ∈ "W' ". The projection "P" has matrix representation

:

A straightforward calculation shows that "W" = Ran "P", the range of "P", is invariant under "T" if and only of "PTP" = "TP". In other words, a subspace "W" being an element of Lat("T") is equivalent to the corresponding projection satisfying the relation "PTP" = "TP".

If "P" is a projection (i.e. "P"² = "P"), so is 1 - "P", where 1 is the identity operator. It follows from the above that "TP = PT" if and only if both Ran "P" and Ran (1 - "P") are invariant under "T". In that case, "T" has matrix representation

:

Colloquially, a projection that commutes with "T" "diagonalizes" "T".

Invariant subspace problem

:main|Invariant subspace problem

The invariant subspace problem concerns the case where "V" is a separable Hilbert space over the complex numbers, of dimension > 1, and "T" is a bounded operator. It asks whether "T" always has a non-trivial closed invariant subspace. This problem is unsolved as of 2006. In case "V" is only assumed to be a Banach space, it was shown in 1975 by P. Enflo and in 1984 by Charles Read that there are counterexamples.

Invariant-subspace lattice

Given a nonempty Σ ⊂ "L"("V"), the invariant subspaces invariant under each element of Σ form a lattice, sometimes called the invariant-subspace lattice of Σ and denoted by Lat(Σ).

The lattice operations are defined in a natural way: for Σ' ⊂ Σ, the "meet" operation is defined by

:

while the "join" operation is

:

A minimal element in Lat(Σ) in said to be a minimal invariant subspace.

Fundamental theorem of noncommutative algebra

Just as the fundamental theorem of algebra ensures that every linear transformation acting on a finite dimensional complex vector space has a nontrivial invariant subspace, the "fundamental theorem of noncommutative algebra" asserts that Lat(Σ) contains nontrivial elements for certain Σ.

Theorem (Burnside) Assume "V" is a complex vector space of dimension greater than 2. For every proper subalgebra Σ of "L"("V"), Lat(Σ) contain a nontrivial element.

Burnside's theorem is of fundamental importance in linear algebra. One consequence is that every commuting family in "L"("V") can be simultaneously upper-triangularized.

A nonempty Σ ⊂ "L"("V") is said to be triangularizable if there exists a basis {"e"₁..."e_n"} of "V" such that

:$mbox\{span\}\; \{\; e\_1,\; cdots,\; e\_k\; \}\; in\; mbox\{Lat\}(Sigma)\; quad\; forall\; k\; geq\; 1\; ;.$

In other words, Σ is triangularizable if there exists a basis such that every element of Σ has an upper-triangular matrix representation in that basis. It follows from Burnside's theorem that every commutative algebra Σ in "L"("V") is triangularizable. Hence every commuting family in "L"("V") can be simultaneously upper-triangularized.

Left ideals

If "A" is an algebra, one can define a "left regular representation" Φ on "A": Φ("a")"b" = "ab" is a homomorphism from "A" to "L"("A"), the algebra of linear transformations on "A"

The invariant subspaces of Φ are precisely the left ideals of "A". A left ideal "M" of "A" gives a subrepresentation of "A" on "M".

If "M" is a left ideal of "A". Consider the quotient vector space "A"/"M". The left regular representation Φ on "M" now descends to a representation Φ' on "A"/"M". If ["b"] denotes an equivalence class in "A"/"M", Φ'("a") ["b"] = ["ab"] . The kernel of the representation Φ' is the set {"a" ∈ "A"| "ab" ∈ "M" for all "b"}.

The representation Φ' is irreducible if and only if "M" is a maximal left ideal, since a subspace "V" ⊂ "A"/"M" is an invariant under {Φ'("a")| "a" ∈ "A"} if and only if its preimage under the quotient map, "V" + "M", is a left ideal in "A".