Math 2.0

Linear and multilinear algebra; matrix theory Definition

Inner product

Builds on Vector space

Definition

Let 𝕂\mathbb{K} be ℝ\R or ℂ\C, and let VV be a vector space over 𝕂\mathbb{K}. An inner product on VV is a map ⟨⋅,⋅⟩ ⁣:V×V→𝕂\langle \cdot, \cdot \rangle \colon V \times V \to \mathbb{K} such that, for all x,y,z∈Vx, y, z \in V and a,b∈𝕂a, b \in \mathbb{K}:

  1. ⟨ax+by,z⟩=a⟨x,z⟩+b⟨y,z⟩\langle ax + by, z \rangle = a \langle x, z \rangle + b \langle y, z \rangle.
  2. ⟨y,x⟩=⟨x,y⟩‾\langle y, x \rangle = \overline{\langle x, y \rangle}.
  3. ⟨x,x⟩>0\langle x, x \rangle > 0 if x≠0x \ne 0.

Remarks

Over ℝ\R, the conjugation does nothing, so an inner product is a symmetric positive definite bilinear form. Over ℂ\C, conditions 1 and 2 give ⟨x,by⟩=bˉ⟨x,y⟩\langle x, by \rangle = \bar b \langle x, y \rangle, so the map is conjugate linear in the second argument. Many physicists, and Mathlib, use the opposite convention: linear in the second argument. The dot product on ℝn\R^n and ⟨x,y⟩=∑ixiyi‾\langle x, y \rangle = \sum_i x_i \overline{y_i} on ℂn\C^n are inner products.

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Further reading

  1. Web https://en.wikipedia.org/wiki/Inner_product_space, Definition
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