Eigenvectors and eigenvalues #
This file defines eigenspaces, eigenvalues, and eigenvalues, as well as their generalized counterparts. We follow Axler's approach [axler2015] because it allows us to derive many properties without choosing a basis and without using matrices.
An eigenspace of a linear map f
for a scalar μ
is the kernel of the map (f - μ • id)
. The
nonzero elements of an eigenspace are eigenvectors x
. They have the property f x = μ • x
. If
there are eigenvectors for a scalar μ
, the scalar μ
is called an eigenvalue.
There is no consensus in the literature whether 0
is an eigenvector. Our definition of
HasEigenvector
permits only nonzero vectors. For an eigenvector x
that may also be 0
, we
write x ∈ f.eigenspace μ
.
A generalized eigenspace of a linear map f
for a natural number k
and a scalar μ
is the kernel
of the map (f - μ • id) ^ k
. The nonzero elements of a generalized eigenspace are generalized
eigenvectors x
. If there are generalized eigenvectors for a natural number k
and a scalar μ
,
the scalar μ
is called a generalized eigenvalue.
The fact that the eigenvalues are the roots of the minimal polynomial is proved in
LinearAlgebra.Eigenspace.Minpoly
.
The existence of eigenvalues over an algebraically closed field
(and the fact that the generalized eigenspaces then span) is deferred to
LinearAlgebra.Eigenspace.IsAlgClosed
.
References #
- [Sheldon Axler, Linear Algebra Done Right][axler2015]
- https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
Tags #
eigenspace, eigenvector, eigenvalue, eigen
The submodule eigenspace f μ
for a linear map f
and a scalar μ
consists of all vectors x
such that f x = μ • x
. (Def 5.36 of [axler2015])
Equations
- f.eigenspace μ = LinearMap.ker (f - (algebraMap R (Module.End R M)) μ)
Instances For
A nonzero element of an eigenspace is an eigenvector. (Def 5.7 of [axler2015])
Instances For
A scalar μ
is an eigenvalue for a linear map f
if there are nonzero vectors x
such that f x = μ • x
. (Def 5.5 of [axler2015])
Instances For
The eigenvalues of the endomorphism f
, as a subtype of R
.
Equations
- f.Eigenvalues = { μ : R // f.HasEigenvalue μ }
Instances For
Equations
- ↑f = Subtype.val
Instances For
Equations
- Module.End.Eigenvalues.instCoeOut = { coe := ↑f }
Equations
- Module.End.Eigenvalues.instDecidableEq f = inferInstanceAs (DecidableEq { x : R // f.HasEigenvalue x })
A nilpotent endomorphism has nilpotent eigenvalues.
See also LinearMap.isNilpotent_trace_of_isNilpotent
.
Alias of the reverse direction of Module.End.hasEigenvalue_iff_mem_spectrum
.
The generalized eigenspace for a linear map f
, a scalar μ
, and an exponent k ∈ ℕ
is the
kernel of (f - μ • id) ^ k
. (Def 8.10 of [axler2015]). Furthermore, a generalized eigenspace for
some exponent k
is contained in the generalized eigenspace for exponents larger than k
.
Equations
- f.genEigenspace μ = { toFun := fun (k : ℕ) => LinearMap.ker ((f - (algebraMap R (Module.End R M)) μ) ^ k), monotone' := ⋯ }
Instances For
A nonzero element of a generalized eigenspace is a generalized eigenvector. (Def 8.9 of [axler2015])
Instances For
A scalar μ
is a generalized eigenvalue for a linear map f
and an exponent k ∈ ℕ
if there
are generalized eigenvectors for f
, k
, and μ
.
Instances For
The generalized eigenrange for a linear map f
, a scalar μ
, and an exponent k ∈ ℕ
is the
range of (f - μ • id) ^ k
.
Equations
- f.genEigenrange μ k = LinearMap.range ((f - (algebraMap R (Module.End R M)) μ) ^ k)
Instances For
The exponent of a generalized eigenvalue is never 0.
The union of the kernels of (f - μ • id) ^ k
over all k
.
Instances For
If there exists a natural number k
such that the kernel of (f - μ • id) ^ k
is the
maximal generalized eigenspace, then this value is the least such k
. If not, this value is not
meaningful.
Equations
- f.maxGenEigenspaceIndex μ = monotonicSequenceLimitIndex (f.genEigenspace μ)
Instances For
For an endomorphism of a Noetherian module, the maximal eigenspace is always of the form kernel
(f - μ • id) ^ k
for some k
.
A generalized eigenvalue for some exponent k
is also
a generalized eigenvalue for exponents larger than k
.
The eigenspace is a subspace of the generalized eigenspace.
All eigenvalues are generalized eigenvalues.
All generalized eigenvalues are eigenvalues.
Generalized eigenvalues are actually just eigenvalues.
Every generalized eigenvector is a generalized eigenvector for exponent finrank K V
.
(Lemma 8.11 of [axler2015])
Generalized eigenspaces for exponents at least finrank K V
are equal to each other.
The restriction of f - μ • 1
to the k
-fold generalized μ
-eigenspace is nilpotent.
The restriction of f - μ • 1
to the generalized μ
-eigenspace is nilpotent.
The eigenspaces of a linear operator form an independent family of subspaces of M
. That is,
any eigenspace has trivial intersection with the span of all the other eigenspaces.
Eigenvectors corresponding to distinct eigenvalues of a linear operator are linearly independent. (Lemma 5.10 of [axler2015])
We use the eigenvalues as indexing set to ensure that there is only one eigenvector for each
eigenvalue in the image of xs
.
If f
maps a subspace p
into itself, then the generalized eigenspace of the restriction
of f
to p
is the part of the generalized eigenspace of f
that lies in p
.
If p
is an invariant submodule of an endomorphism f
, then the μ
-eigenspace of the
restriction of f
to p
is a submodule of the μ
-eigenspace of f
.
Generalized eigenrange and generalized eigenspace for exponent finrank K V
are disjoint.
If an invariant subspace p
of an endomorphism f
is disjoint from the μ
-eigenspace of f
,
then the restriction of f
to p
has trivial μ
-eigenspace.
The generalized eigenspace of an eigenvalue has positive dimension for positive exponents.
A linear map maps a generalized eigenrange into itself.