中文

In matrix theory it is common to say that a matrix is symmetric if it is equal to its transpose, . This concept does not however transfer meaningfully to the matrix of a linear operator on a vector space unless some extra structure is imposed on that space. For example, let : be an operator whose matrix is symmetric with respect to a specific basis. Under a change of basis the transformed matrix is , while for the transpose matrix

Hence in general. We should hardly be surprised by this conclusion for, as commented at the beginning of Chapter 4, the component equation violates the index conventions of Section 3.6.

Show that is symmetric if and only if commutes with

Thus the concept of a ‘symmetric operator’ is not invariant under general basis transformations, bu it is invariant with respect to orthogonal basis transformations,

If is a complex vector space it is similarly meaningless to talk of an operator as being ‘hermitian’ if its matrix with respect to some basis is hermitian,

Show that the hermitian property is not in general basis invariant, but is preserved under unitary transformations, where

In this chapter we shall see that symmetric and hermitian matrices play a different role in vector space theory, in that they represent inner products instead of operators [1–3]. Matrices representing inner products are best written with both indices on the subscript level, and . The requirements of symmetry and hermiticity are not then at odds with the index conventions.

Contents

Concept index

Terms by section. Links lead to the brown underlined definitions in the Chinese reading text.

5.1 Real inner product spaces

5.2 Complex inner product spaces

5.3 Representations of finite groups