Inner Product Spaces | Mathematics

An inner product on a vector space $V$ over $F$ (where $F = \mathbb{R}$ or $\mathbb{C}$) is a Function $\langle \cdot, \cdot \rangle : V \times V \to F$ satisfying:

1. Conjugate symmetry: $\langle \mathbf{u}, \mathbf{v} \rangle = \overline{\langle \mathbf{v}, \mathbf{u} \rangle}$
2. Linearity in the first argument: $\langle \alpha\mathbf{u} + \beta\mathbf{w}, \mathbf{v} \rangle = \alpha\langle \mathbf{u}, \mathbf{v} \rangle + \beta\langle \mathbf{w}, \mathbf{v} \rangle$
3. Positive definiteness: $\langle \mathbf{v}, \mathbf{v} \rangle \geq 0$ with equality iff $\mathbf{v} = \mathbf{0}$

A vector space equipped with an inner product is called an inner product space.

Example. The standard inner product on $\mathbb{R}^n$ is $\langle \mathbf{x}, \mathbf{y} \rangle = \sum_{i=1}^n x_i y_i$. On $\mathbb{C}^n$ $\langle \mathbf{x}, \mathbf{y} \rangle = \sum_{i=1}^n x_i \overline{y_i}$.

Example. On $C[a,b]$The $L^2$ inner product is $\langle f, g \rangle = \int_a^b f(x)g(x)\,dx$.

Every inner product induces a norm:

$\lVert \mathbf{v} \rVert = \sqrt{\langle \mathbf{v}, \mathbf{v} \rangle}$

Theorem 7.1 (Cauchy—Schwarz Inequality). For all $\mathbf{u}, \mathbf{v} \in V$

$\lvert\langle \mathbf{u}, \mathbf{v} \rangle\rvert \leq \lVert \mathbf{u} \rVert \, \lVert \mathbf{v} \rVert$

With equality if and only if $\mathbf{u}$ and $\mathbf{v}$ are linearly dependent.

Proof. If $\mathbf{v} = \mathbf{0}$Both sides are 0 and the result holds. Assume $\mathbf{v} \neq \mathbf{0}$. For any $t \in \mathbb{R}$ (or $\mathbb{C}$), positive definiteness gives

$0 \leq \langle \mathbf{u} - t\mathbf{v}, \mathbf{u} - t\mathbf{v} \rangle = \langle \mathbf{u}, \mathbf{u} \rangle - t\langle \mathbf{v}, \mathbf{u} \rangle - \overline{t}\langle \mathbf{u}, \mathbf{v} \rangle + \lvert t \rvert^2 \langle \mathbf{v}, \mathbf{v} \rangle$

Set $t = \frac{\langle \mathbf{u}, \mathbf{v} \rangle}{\langle \mathbf{v}, \mathbf{v} \rangle}$ (the value that minimises the right side):

$0 \leq \lVert \mathbf{u} \rVert^2 - \frac{\lvert\langle \mathbf{u}, \mathbf{v} \rangle\rvert^2}{\lVert \mathbf{v} \rVert^2}$

Rearranging: $\lvert\langle \mathbf{u}, \mathbf{v} \rangle\rvert^2 \leq \lVert \mathbf{u} \rVert^2 \lVert \mathbf{v} \rVert^2$. Taking square roots gives the result. Equality holds iff $\mathbf{u} - t\mathbf{v} = \mathbf{0}$ I.e., $\mathbf{u}$ and $\mathbf{v}$ are linearly dependent. $\blacksquare$

Theorem 7.2 (Triangle Inequality).

$\lVert \mathbf{u} + \mathbf{v} \rVert \leq \lVert \mathbf{u} \rVert + \lVert \mathbf{v} \rVert$

Proof.

$\lVert \mathbf{u} + \mathbf{v} \rVert^2 = \langle \mathbf{u} + \mathbf{v}, \mathbf{u} + \mathbf{v} \rangle = \lVert \mathbf{u} \rVert^2 + 2\,\mathrm{Re}\langle \mathbf{u}, \mathbf{v} \rangle + \lVert \mathbf{v} \rVert^2$

By Cauchy—Schwarz, $\mathrm{Re}\langle \mathbf{u}, \mathbf{v} \rangle \leq \lvert\langle \mathbf{u}, \mathbf{v} \rangle\rvert \leq \lVert \mathbf{u} \rVert \lVert \mathbf{v} \rVert$ So

$\lVert \mathbf{u} + \mathbf{v} \rVert^2 \leq \lVert \mathbf{u} \rVert^2 + 2\lVert \mathbf{u} \rVert \lVert \mathbf{v} \rVert + \lVert \mathbf{v} \rVert^2 = (\lVert \mathbf{u} \rVert + \lVert \mathbf{v} \rVert)^2$

Taking square roots gives the result. $\blacksquare$

Two vectors $\mathbf{u}, \mathbf{v}$ are orthogonal if $\langle \mathbf{u}, \mathbf{v} \rangle = 0$. We write $\mathbf{u} \perp \mathbf{v}$.

An orthonormal set $\{e_1, \ldots, e_k\}$ satisfies $\langle e_i, e_j \rangle = \delta_{ij}$.

Theorem 7.3 (Pythagorean Theorem). If $\mathbf{u} \perp \mathbf{v}$ Then

$\lVert \mathbf{u} + \mathbf{v} \rVert^2 = \lVert \mathbf{u} \rVert^2 + \lVert \mathbf{v} \rVert^2$

Proof. $\lVert \mathbf{u} + \mathbf{v} \rVert^2 = \lVert \mathbf{u} \rVert^2 + 2\langle \mathbf{u}, \mathbf{v} \rangle + \lVert \mathbf{v} \rVert^2 = \lVert \mathbf{u} \rVert^2 + \lVert \mathbf{v} \rVert^2$. $\blacksquare$

Proposition 7.4. Every orthonormal set is linearly independent.

Proof. If $\sum_{i=1}^k \alpha_i e_i = \mathbf{0}$ Then taking the inner product with $e_j$: $\alpha_j = \langle \sum \alpha_i e_i, e_j \rangle = \langle \mathbf{0}, e_j \rangle = 0$ for each $j$. $\blacksquare$

The Gram—Schmidt process converts a linearly independent set $\{\mathbf{v}_1, \ldots, \mathbf{v}_n\}$ into an orthonormal set $\{e_1, \ldots, e_n\}$:

$\mathbf{u}_1 = \mathbf{v}_1, \quad e_1 = \frac{\mathbf{u}_1}{\lVert \mathbf{u}_1 \rVert}$

$\mathbf{u}_k = \mathbf{v}_k - \sum_{i=1}^{k-1} \langle \mathbf{v}_k, e_i \rangle e_i, \quad e_k = \frac{\mathbf{u}_k}{\lVert \mathbf{u}_k \rVert}$

Proposition 7.5. At each step, $\mathrm{span}\{e_1, \ldots, e_k\} = \mathrm{span}\{\mathbf{v}_1, \ldots, \mathbf{v}_k\}$.

Proof. By construction, $\mathbf{u}_k$ is $\mathbf{v}_k$ minus its projection onto $\mathrm{span}\{e_1, \ldots, e_{k-1}\} = \mathrm{span}\{\mathbf{v}_1, \ldots, \mathbf{v}_{k-1}\}$. So $\mathbf{u}_k \in \mathrm{span}\{\mathbf{v}_1, \ldots, \mathbf{v}_k\}$ and $\mathbf{v}_k = \mathbf{u}_k + \sum_{i=1}^{k-1}\langle \mathbf{v}_k, e_i \rangle e_i \in \mathrm{span}\{\mathbf{u}_1, \ldots, \mathbf{u}_k\}$. Since each $e_i$ is a scalar multiple of $\mathbf{u}_i$The spans coincide. $\blacksquare$

The orthogonal projection of $\mathbf{v}$ onto a subspace $W$ with orthonormal basis $\{e_1, \ldots, e_k\}$ is

$\mathrm{proj_W}(\mathbf{v}) = \sum_{i=1}^k \langle \mathbf{v}, e_i \rangle e_i$

Theorem 7.6 (Best Approximation). Among all vectors in $W$The orthogonal projection $\mathrm{proj_W}(\mathbf{v})$ minimises the distance to $\mathbf{v}$:

$\lVert \mathbf{v} - \mathrm{proj_W}(\mathbf{v}) \rVert \leq \lVert \mathbf{v} - \mathbf{w} \rVert \quad \mathrm{for}~all~ \mathbf{w} \in W$

Proof. For any $\mathbf{w} \in W$Write $\mathbf{v} - \mathbf{w} = (\mathbf{v} - \mathrm{proj_W}(\mathbf{v})) + (\mathrm{proj_W}(\mathbf{v}) - \mathbf{w})$. The first term is orthogonal to $W$ (hence to the second term, which lies in $W$), so by the Pythagorean theorem:

$\lVert \mathbf{v} - \mathbf{w} \rVert^2 = \lVert \mathbf{v} - \mathrm{proj_W}(\mathbf{v}) \rVert^2 + \lVert \mathrm{proj_W}(\mathbf{v}) - \mathbf{w} \rVert^2 \geq \lVert \mathbf{v} - \mathrm{proj_W}(\mathbf{v}) \rVert^2$

With equality iff $\mathbf{w} = \mathrm{proj_W}(\mathbf{v})$. $\blacksquare$

A fundamental application of orthogonal projection is fitting functions to data. Given a subspace $W$ of an inner product space $V$ and a target $\mathbf{v} \in V$The best approximation in $W$ Is the orthogonal projection $\mathrm{proj_W}(\mathbf{v})$.

Problem. Apply the Gram—Schmidt process to $\mathbf{v}_1 = (1, 1, 0)$ $\mathbf{v}_2 = (1, 0, 1)$, $\mathbf{v}_3 = (0, 1, 1)$ in $\mathbb{R}^3$ with the standard inner Product.

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