Topological properties

For properties which arise when viewed as metric space, eg: compactness, boundedness, connectedness etc.., see topology ref.

Properties of \(R^{n, C^{n}\)

See properties of k-cells in R in analysis of functions over fields ref.

Completeness of \(R^{n, C^{n}\)

Take vector seq \((x_{i}) \to x\). If V is over R or C, \((x_{i}) \to x\) iff it is a Cauchy seq wrt norm: from equiv property over R and C.

Dimension of V

This is the maximal size of any linearly independent set of vectors in V.

Basis of vector space V

\(T = \set{t_i}: \forall v\in V: v = \sum a_i t_i\), such that \(T\) is linearly independent. Often written as a matrix T, so that we can write \(Ta = v\) for any v in V. Any maximal set of independent vectors in V is a basis. All bases (eg T and T’) have the same size: otherwise, you would have a contradiction. So, \(|T| = dim(V)\).

Orthonormal and standard basis

Orthonormal basis: \(t_i^{T}t_j = 0, \dprod{t_i, t_i} = 1\). Standard basis: see section on definiton of vectors.

Can get an orthogonal basis using QR making algorithms.

Subspaces

Aka Linear subspace. For subspaces associated with a linear operator, see linear algebra ref.

Membership conditions

A vector \(v\neq 0\) is in a subspace S iff it is some linear combination of its basis Q; so \(v = Qx\).

This happens only if \(\exists i: v^{T}q_i \neq 0\): so v is not \(\perp Q\) wrt standard inner product.

Invariant subspace

S is an invariant subspace of A if \(AS \subseteq S\).