• Title: Orthonormal System and Orthonormal Basis

  • Series: Hilbert Spaces

  • Chapter: ONS and ONB

  • YouTube-Title: Hilbert Spaces 10 | Orthonormal System and Orthonormal Basis

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  • Quiz: Test your knowledge

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  • Subtitle on GitHub: hs10_sub_eng.srt missing

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  • Definitions in the video: ONS, ONB, Orthonormal Basis, Orthonormal System, total set

  • Timestamps

    00:00 Introduction

    00:38 Notion: orthonormal

    01:51 Definition: (infinite) ONS and ONB

    03:39 Notion: total system

    06:20 Example: Hilbert space of sequences

    09:57 Credits

  • Subtitle in English (n/a)
  • Quiz Content

    Q1: Let $X$ be a vector space with norm $\| \cdot \|$. What does it mean that a set $M \subseteq X$ is dense in $X$?

    A1: For all $x \in X$ and all $\varepsilon > 0$, there exists an element $m \in M$ such that $ \| x - m \| < \varepsilon$.

    A2: For all $x \in X$ and there is an $\varepsilon > 0$ such that for all $m \in M$ such that $ \| x - m \| < \varepsilon$.

    A3: There is an $x \in X$ and there is an $\varepsilon > 0$ such that for all $m \in M$ such that $ \| x - m \| < \varepsilon$.

    A4: There is an $x \in X$ such that for all $\varepsilon > 0$ and all $m \in M$ such that $ \| x - m \| < \varepsilon$.

    Q2: Let $X$ be a vector space with norm $\| \cdot \|$. What does it mean that a set $M \subseteq X$ is total in $X$?

    A1: The subspace $\mathrm{Span}(M)$ is dense in X.

    A2: The subspace $\mathrm{Span}(M)$ is equal X.

    A3: There is an $x \in X$ and there is an $\varepsilon > 0$ such that for all $m \in M$ such that $ \| x - m \| < \varepsilon$.

    A4: For all $x \in X$ and all $\varepsilon > 0$, there exists an element $m \in M$ such that $ \| x - m \| < \varepsilon$.

    Q3: Let $X$ be a inner product space. What is the definition of an ONB in it?

    A1: It’s an ONS that is also total.

    A2: It’s an ONS that is also as basis of the vector space $X$.

    A3: It’s an ONS that is also dense in $X$.

    A4: It’s an ONS with the property $\langle e_{\alpha} , e_{\beta} \rangle = \delta_{\alpha, \beta}$.

  • Date of video: 2025-03-11

  • Last update: 2026-10

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