Dual space by Ciro Santilli 40 Updated 2025-07-16
The dual space of a vector space , sometimes denoted , is the vector space of all linear forms over with the obvious addition and scalar multiplication operations defined.
Since a linear form is completely determined by how it acts on a basis, and since for each basis element it is specified by a scalar, at least in finite dimension, the dimension of the dual space is the same as the , and so they are isomorphic because all vector spaces of the same dimension on a given field are isomorphic, and so the dual is quite a boring concept in the context of finite dimension.
One place where duals are different from the non-duals however is when dealing with tensors, because they transform differently than vectors from the base space .
Linear operator by Ciro Santilli 40 Updated 2025-07-16
We define it as a linear map where the domain is the same as the image, i.e. an endofunction.
Examples:
Adjoint operator by Ciro Santilli 40 Updated 2025-07-16
Given a linear operator over a space that has a inner product defined, we define the adjoint operator (the symbol is called "dagger") as the unique operator that satisfies:
Bilinear form by Ciro Santilli 40 Updated 2025-07-16
Analogous to a linear form, a bilinear form is a Bilinear map where the image is the underlying field of the vector space, e.g. .
Some definitions require both of the input spaces to be the same, e.g. , but it doesn't make much different in general.
The most important example of a bilinear form is the dot product. It is only defined if both the input spaces are the same.
If is the change of basis matrix, then the matrix representation of a bilinear form that looked like:
then the matrix in the new basis is:
Sylvester's law of inertia then tells us that the number of positive, negative and 0 eigenvalues of both of those matrices is the same.
Proof: the value of a given bilinear form cannot change due to a change of basis, since the bilinear form is just a function, and does not depend on the choice of basis. The only thing that change is the matrix representation of the form. Therefore, we must have:
and in the new basis:
and so since:
Symmetric bilinear map by Ciro Santilli 40 Updated 2025-07-16
Subcase of symmetric multilinear map:
Requires the two inputs and to be in the same vector space of course.
The most important example is the dot product, which is also a positive definite symmetric bilinear form.
Hermitian form by Ciro Santilli 40 Updated 2025-07-16
The prototypical example of it is the complex dot product.
Note that this form is neither strictly symmetric, it satisfies:
where the over bar indicates the complex conjugate, nor is it linear for complex scalar multiplication on the second argument.
The normal navigation to them was paywalled, but the static files are served without login checks if you know their URL. One way to go about it is to search by prefix on the Wayback Machine: web.archive.org/web/*/https://www2.physics.ox.ac.uk/sites/default/files/contentblock/2011/06/03/*
The last handbooks we can find are 2020/2021, they might have move to a new more properly paywalled location after that year.
Representation theory by Ciro Santilli 40 Updated 2025-07-16
Basically, a "representation" means associating each group element as an invertible matrices, i.e. a matrix in (possibly some subset of) , that has the same properties as the group.
Or in other words, associating to the more abstract notion of a group more concrete objects with which we are familiar (e.g. a matrix).
Each such matrix then represents one specific element of the group.
This is basically what everyone does (or should do!) when starting to study Lie groups: we start looking at matrix Lie groups, which are very concrete.
Or more precisely, mapping each group element to a linear map over some vector field (which can be represented by a matrix infinite dimension), in a way that respects the group operations:
As shown at Physics from Symmetry by Jakob Schwichtenberg (2015)
Bibliography:

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