Linear algebra (Osnabrück 2024-2025)/Part I/Lecture 13/refcontrol
- Projections
For a direct sum decompositionMDLD/direct sum decomposition of a -vector spaceMDLD/vector space , the linear mappingMDLD/linear mapping
is called the first projection (or projection onto with respect to the given decomposition or projection onto along ) and, accordingly,
the second projection of this decomposition. Since and are linear subspaces of , it is reasonable to call the composed mapping
a projection as well. Then we have a projection in the sense of the following definition.
Let be a field,MDLD/field a -vector spaceMDLD/vector space and a linear subspace.MDLD/linear subspace A linear mappingMDLD/linear mapping
is called a projection of onto , if and
holds.== Example Example 13.2
change==
Let be a finite-dimensionalMDLD/finite-dimensional (vs) -vector spaceMDLD/vector space and , , a basisMDLD/basis (vs) of . For a subset , set
the linear subspaceMDLD/linear subspace corresponding to . Moreover, let
the corresponding projection.MDLD/projection (direct sum) The image of this projection is . On , this mapping is the identity, one can also consider this mapping as
The kernelMDLD/kernel (vs) of this mapping is
For , equipped with the standard basis,MDLD/standard basis we consider subsets with two elements and the corresponding projections[1] (in the sense of Example 13.2 ) onto the coordinate planes. The projections are
The images of some object in under these projections carry names like ground view etc.
For a subset with one element, the projections map to an axis.
A more abstract definition is the following that does not refer to a linear subspace.
Let be a fieldMDLD/field and a -vector space.MDLD/vector space A linear mappingMDLD/linear mapping
is called a projection, if
The identity and the zero mapping are projections.
Let be a fieldMDLD/field and a -vector space.MDLD/vector space For a direct sum decompositionMDLD/direct sum decomposition
the projectionMDLD/projection (direct sum) onto is a projection in the sense of Definition.MDLD/Definition Such a projection
gives a decomposition
Let be the projection onto . Write with . Then we have
and hence
Suppose now that
is an endomorphism with
Let . Then there exists some such that
Then
This means that the intersection of these linear subspaces is the zero space. For an arbitrary , we write
Here, the first summand belongs to the image and, because of
the second summand belongs to the kernel. Therefore, we have a direct sum decomposition.
- Spaces of homomorphisms
Let be a field,MDLD/field and let and be -vector spaces.MDLD/vector spaces Then
is called the space of homomorphisms. It is endowed with the addition defined by
and the scalar multiplication defined by
Due to Exercise 13.8 , this is indeed a -vector space.
Let be a -vector spaceMDLD/vector space over the fieldMDLD/field . Then the mappingMDLD/mapping
is an isomorphismMDLD/isomorphism (vs) of vector spaces, see Exercise 13.10 .
The space of homomorphisms plays an important role. It is called the dual space of . In the next lectures, we will discuss it in more details.
Let be a field,MDLD/field and let and be
-vector spaces.MDLD/vector spaces Then the following hold.- A
linear mappingMDLD/linear mapping
from another vector space induces a linear mapping
- A
linear mappingMDLD/linear mapping
to another vector space induces a linear mapping
Proof
Let be a -vector spaceMDLD/vector space together with a direct sum decompositionMDLD/direct sum decomposition
Let be another -vector space and let
and
denote linear mappings.MDLD/linear mappings Then we get, by setting
where is the direct decomposition, a linear mapping
The mapping is well-defined, since the representation with and is unique. The linearity follows from
Let be a field,MDLD/field and let and be -vector spaces.MDLD/vector spaces Let
and
de direct sum decompositionsMDLD/direct sum decompositions and let
denote the canonical projections.MDLD/canonical projections (direct sum) Then the mapping
is an isomorphism.MDLD/isomorphism (vs) If we consider as linear subspaces of , then we have the direct sum decomposition
It follows directly from Lemma 13.8 that the given mapping is linear. In order to prove injectivity, let with be given. Then there exists some such that
Let with . Then also for some . Therefore, for some . Hence
In order to prove surjectivity, let a family of homomorphisms , be given, which we consider as mappings to . Then the
are linear mappings from to . This yields via Lemma 13.9 a linear mapping from to , which restricts to the given mappings.
Let denote a fieldMDLD/field and let and denote finite-dimensionalMDLD/finite-dimensional -vector spaces.MDLD/vector spaces Let be a basisMDLD/basis (vs) of and be a basis of . Then the assignment
is an isomorphismMDLD/isomorphism (vs)
of -vector spaces.The bijectivity was shown in Theorem 10.15 . The additivity follows from
where the index denotes the -th component with respect to the basis .
This means that we can consider the direct sum decomposition for the one-dimensional linear subspaces
bzw.
corresponding to bases and apply
Lemma 13.10
.
Let denote a field,MDLD/field and let and denote finite-dimensionalMDLD/finite-dimensional -vector spacesMDLD/vector spaces with dimensionsMDLD/dimensions (vs) and . Then
This follows immediately from Theorem 13.11 .
- Linear subspaces of homomorphism spaces
Let and be vector spacesMDLD/vector spaces over a fieldMDLD/field . Then the following subsets are linear subspacesMDLD/linear subspaces
of .- For a linear subspace
,
is a linear subspace of . If and are finite-dimensional,MDLD/finite-dimensional then
- For a linear subspace
,
is a linear subspace of , which is isomorphicMDLD/isomorphic (linear) to . If and are finite-dimensional,MDLD/finite-dimensional then
- For linear subspaces
and
,
is a linear subspace of . If and finite-dimensional,MDLD/finite-dimensional then
- For linear subspaces
and
,
is a linear subspace of .
(1). It is clear that we have a linear subspace. In order to prove the statement about the dimension, let be an direct complementMDLD/direct complement of in , so that
Let be a basis of . Every linear mapping from maps to , and on (or on a basis thereof) we have free choice. Therefore
and the statement follows from Corollary 13.12 .
(2). It is clear that we have a linear subspace. The natural mapping
of Lemma 13.8 (2) is injective in this case. Therefore,
(3). It is clear that we have a linear subspace. In the finite-dimensional case, let
be a direct sum decomposition. Due to Lemma 13.10 , we have
and
Therefore, the dimension equals
(4). Setting
we have . Hence, (4) follows from (3).
Let denote a fieldMDLD/field and let and denote finite-dimensionalMDLD/finite-dimensional -vector spaces.MDLD/vector spaces We consider the natural mapping
where we have the product space on the left. This mapping is not linear in general. We have, on one hand,
and, on the other hand,
so, if we fix one component, we have additivity (and also compatibility with the scalar multiplication) in the other component. In the product space, we have
and, therefore,
(only in exceptional cases we have ).
- Footnotes
- ↑ These projections are even orthogonal projections. This term requires that an inner product is defined, a concept we will discuss in the second term. Here, we only deal with linear projections, which depend, different from the orthogonal case, from the direct complement chosen.
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