Vector Space

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tags
Linear Algebra, Math

A vector space is a set of vectors together with rules for vector addition and multiplication by scalars. The addition and multiplication rules must be consistent with the following properties (for \(x\in\mathbb{F}^n\), \(c\in\mathbb{F}\), \(\mathbb{F} = \{\mathbb{R}, \mathbb{C}\}\) ):

  1. \(\xvec+\yvec = \yvec+\xvec\)
  2. \(\xvec+(\yvec+\zvec) = (\xvec+\yvec) + \zvec\)
  3. There is a unique zero vector \(\mathbf{0} + \xvec = \xvec\)
  4. For each \(\xvec\) there is a unique vector \(-\xvec\) such that \(\xvec + (-\xvec) = \mathbf{0}\).
  5. \(1\xvec = \xvec\)
  6. \((c_1c_2)\xvec = c_1(c_2\xvec)\)
  7. \(c(\xvec + \yvec) = c\xvec + c\yvec\)
  8. \((c_1 + c_2)\xvec = c_1\xvec + c_2\xvec\)

A vector space is a set of vectors which is closed under rules for vector addition and multiplication by scalars (i.e. rules to take linear combinations of vectors).

Vector Subspaces

A vector subspace is a non-empyt subset of a vector space which is closed under the same linear combination rules. The most trivial vector subspace of \(\reals^n\) is the set containing the zero vector.

A subspace of a vector space is a nonempty subset that satisfies the requirements for a vector space: Linear combinations stay in the subspace.

Column (row) space of a matrix

The column space of a matrix is all possible linear combinations of the columns of a matrix. The row space is the same except with linear combinations of the rows of a matrix.

Nullspace of a matrix

Given a matrix \(\Amat \in \reals^{M\times N}\). The nullspace of \(\Amat\) is all vectors \(\xvec\) such that \(\Amat\xvec = \mathbf{0}\).

The nullspace is:

  • A subspace of \(\reals^M\)

Dual Space

The dual vector space is the set \(V^\star\) of all linear functionals \(f: V \mapsto \mathbb{F}\). Since linear functionals are vector space homomorphisms this is sometimes denoted as \(Hom(V, \mathbb{F})\). The dual space is itself a vector space, meaning (for \(\phi, \psi \in V^\star\) and \(\xvec \in V\))

  1. \((\phi + \psi)(\xvec) = \phi(\xvec) + \psi(\xvec)\)
  2. \((\alpha \phi)(\xvec) = \alpha\phi(\xvec)\).

The dual vector space has the same dimensions as the original vector space, and has a canonical dual basis (or cobasis) \(\theta^j\) defined by the original basis \(e_i\)

\[\langle e_i, \theta^j \rangle = \delta^j_i \quad \triangleright \delta^j_i \text{ is the Kronecker delta function}\].

Notation gets a bit confusing in higher order linear algebra and tensor algebra. Typically, I will attempt to denote an index of the cobasis as an upstairs index while the basis as a downstairs index.

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