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Section 4.4 Abstract Vector Spaces

While \(\mathbb{R}^n\) provides a concrete and familiar setting for studying linear algebra, the concepts we have learned extend far beyond column vectors. Many mathematical objects can be viewed as vectors if they satisfy the fundamental properties of vector addition and scalar multiplication. In this section, we explore several important examples of abstract vector spaces.

Subsection 4.4.1 Polynomial Spaces

Definition 4.4.1. Polynomial Space \(\mathcal{P}_n(x)\).

Let \(\mathcal{P}_n(x)=\{a_0+a_1x+\ldots+a_nx^n|a_i\in \mathbb{R}\}\) be the set of all polynomials of degree at most \(n\) with real coefficients. This set forms a vector space with operations:
Addition:
\begin{gather*} (a_0+a_1x+\ldots+a_nx^n)+(b_0+b_1x+\ldots+b_nx^n)\\ =(a_0+b_0)+(a_1+b_1)x+\ldots+(a_n+b_n)x^n \end{gather*}
Scalar Multiplication:
\begin{equation*} k(a_0+a_1x+\ldots+a_nx^n)=(ka_0)+(ka_1)x+\ldots+(ka_n)x^n \end{equation*}
Standard Basis for \(\mathcal{P}_n(x)\text{:}\) The set \(\{1, x, x^2, \ldots, x^n\}\) forms a basis for \(\mathcal{P}_n(x)\text{.}\) This space has dimension \(n+1\text{.}\)

Subsection 4.4.2 Matrix Spaces

Definition 4.4.2. Matrix Space \(M_{m \times n}(\mathbb{R})\).

Let \(M_{m \times n}(\mathbb{R})=\left\{\left[ \begin{array}{ccc} a_{11} & \ldots & a_{1n} \\ \vdots & & \vdots \\ a_{m1} & \ldots & a_{mn} \\ \end{array} \right]\middle| a_{ij}\in \mathbb{R} \right\}\) be the set of all \(m \times n\) matrices with real entries. This set forms a vector space with the usual matrix addition and scalar multiplication.
Standard Basis for \(M_{m \times n}(\mathbb{R})\text{:}\) The set of matrices \(E_{ij}\) where \(E_{ij}\) has 1 in the \((i,j)\) position and 0 elsewhere forms a basis. This space has dimension \(mn\text{.}\)

Example 4.4.3.

For \(M_{2 \times 2}(\mathbb{R})\text{,}\) the standard basis consists of:
\begin{equation*} E_{11} = \begin{bmatrix}1 & 0\\0 & 0\end{bmatrix}, \quad E_{12} = \begin{bmatrix}0 & 1\\0 & 0\end{bmatrix}, \quad E_{21} = \begin{bmatrix}0 & 0\\1 & 0\end{bmatrix}, \quad E_{22} = \begin{bmatrix}0 & 0\\0 & 1\end{bmatrix} \end{equation*}

Subsection 4.4.3 Function Spaces

Definition 4.4.4. Space of Continuous Functions.

Let \(C(-\infty, \infty)\) be the set of all real-valued continuous functions defined on the entire real line. This set forms a vector space with operations:
Addition: \((f+g)(x)=f(x)+g(x)\)
Scalar Multiplication: \((cf)(x)=c[f(x)]\)

Definition 4.4.5. Space of Continuous Functions on an Interval.

Let \(C[a, b]\) be the set of all real-valued continuous functions defined on the closed interval \([a,b]\text{.}\) This set forms a vector space with the same operations as above.
Note: Unlike the previous examples, \(C(-\infty, \infty)\) and \(C[a,b]\) are infinite-dimensional vector spaces. They do not have finite bases, which makes them more complex to analyze than finite-dimensional spaces.

Example 4.4.6.

The functions \(f(x) = \sin(x)\text{,}\) \(g(x) = \cos(x)\text{,}\) and \(h(x) = e^x\) are all elements of \(C(-\infty, \infty)\text{.}\) The function \(2\sin(x) + 3\cos(x) - e^x\) is their linear combination in this vector space.

Example 4.4.7. Fourier Series of \(f(x)=x\) on \(C[-\pi,\pi]\).

Consider the function \(f(x)=x\) defined on \([-\pi,\pi]\) and extended periodically with period \(2\pi\text{.}\) Find its Fourier series representation on \([-\pi,\pi]\text{.}\)
Solution.
The Fourier series of a function \(f(x)\) on \([-\pi,\pi]\) is given by
\begin{equation*} f(x) \sim \frac{a_0}{2} + \sum_{n=1}^{\infty} \left(a_n \cos(nx) + b_n \sin(nx)\right), \end{equation*}
where the coefficients are computed as
\begin{equation*} a_0 = \frac{1}{\pi}\int_{-\pi}^{\ pi} f(x)\,dx, \quad a_n = \frac{1}{\pi}\int_{-\pi}^{\pi} f(x)\cos(nx)\,dx \end{equation*}
\begin{equation*} b_n = \frac{1}{\pi}\int_{-\pi}^{\pi} f(x)\sin(nx)\,dx. \end{equation*}
Since \(f\) is an odd function on \([-\pi,\pi]\text{,}\) all cosine coefficients vanish. The Fourier series has the form
\begin{equation*} f(x) \sim \sum_{n=1}^{\infty} b_n \sin(nx). \end{equation*}
The sine coefficients are
\begin{equation*} b_n = \frac{1}{\pi}\int_{-\pi}^{\pi} x\sin(nx)\,dx. \end{equation*}
Compute the integral using odd/even symmetry or integration by parts. Because \(x\sin(nx)\) is even, we get
\begin{equation*} b_n = \frac{2}{\pi}\int_{0}^{\pi} x\sin(nx)\,dx = \frac{2}{\pi}\left[\left.-\frac{x\cos(nx)}{n}\right|_{0}^{\pi} + \frac{1}{n}\int_{0}^{\pi}\cos(nx)\,dx\right]. \end{equation*}
Evaluating gives
\begin{equation*} b_n = \frac{2}{\pi}\left(-\frac{\pi\cos(n\pi)}{n} + \frac{\sin(n\pi)}{n^2}\right) = \frac{2}{\pi}\left(-\frac{\pi(-1)^n}{n} + 0\right) = 2\frac{(-1)^{n+1}}{n}. \end{equation*}
Therefore the Fourier series is
\begin{equation*} f(x) \sim 2\sum_{n=1}^{\infty}\frac{(-1)^{n+1}}{n}\sin(nx). \end{equation*}
This series converges to \(f(x)=x\) for all \(x\in(-\pi,\pi)\text{,}\) and at the endpoints \(\pm\pi\) it converges to the midpoint of the left- and right-hand limits, i.e. to 0.
Interpretation: The result shows how an elementary function in the function space \(C[-\pi,\pi]\) (with periodic extension) can be expressed as a linear combination of the orthogonal basis functions \(\{\sin(nx),\cos(nx)\}\text{.}\) This is an instance of representing vectors (functions) in an infinite-dimensional function space by coordinates relative to a chosen basis.

Subsection 4.4.4 Common Properties

All these examples share the fundamental properties that define a vector space:
  • Closure under addition and scalar multiplication
  • Existence of a zero vector (zero polynomial, zero matrix, zero function)
  • Existence of additive inverses
  • Associativity and commutativity of addition
  • Distributive properties of scalar multiplication
Key Insight: The concepts of linear independence, span, basis, and dimension that we studied in \(\mathbb{R}^n\) apply equally well to these abstract vector spaces. This universality is what makes linear algebra such a powerful and widely applicable field of mathematics.