Non-Inner Product Metric Space: Understanding & Examples

In summary, an example of a metric space that is not derived from an inner product between two vectors is the discrete metric, which is defined as follows: d(x,y) = 0 if x=y and d(x,y)=1 if not. Another example is given by the function d(m,n) = |m^-1 - n^-1| for m, n ∈ ℕ, with d(n,∞) = d(∞,n) = 1/n and d(∞,∞) = 0.
  • #1
kthouz
193
0
Can somebody give me an other metric space that is not dependent on the inner product i mean which is not derived from the inner product between two vectors.
 
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  • #2
The function d(x,y) = 0 if x=y and d(x,y)=1 if not. It's called the discrete metric.
 
  • #3
I remember a particularly exotic one given as an example to me, that the details elude me right at the moment. But here's a good one:

[tex]m, n \in \mathbb{N}[/tex]

[tex]d(m,n) = \left| m^{-1} - n^{-1} \right|[/tex]

[tex]d(n,\infty) = d(\infty,n) = \frac{1}{n}[/tex]

[tex]d(\infty,\infty) = 0[/tex]
 

Related to Non-Inner Product Metric Space: Understanding & Examples

1. What is a non-inner product metric space?

A non-inner product metric space is a mathematical concept that describes a set of points with a defined distance function, or metric, that does not satisfy the properties of an inner product. This means that the metric does not follow the rules of symmetry, linearity, and positive definiteness that an inner product would have.

2. How does a non-inner product metric space differ from an inner product metric space?

In an inner product metric space, the metric or distance function follows the rules of symmetry, linearity, and positive definiteness. This allows for the definition of angles, orthogonality, and projections, which are not possible in a non-inner product metric space. Additionally, the inner product allows for the calculation of norms, which cannot be done in a non-inner product metric space.

3. What are some examples of non-inner product metric spaces?

Some examples of non-inner product metric spaces include the space of square-integrable functions, the space of continuous functions, and the space of polynomials with a given degree. These examples do not satisfy the properties of an inner product due to the nature of their metrics.

4. Why are non-inner product metric spaces important?

Non-inner product metric spaces are important in mathematics and science because they provide a more general framework for understanding distance and geometry. They allow for the exploration and study of spaces that do not follow the traditional rules of an inner product, which can be useful in applications such as optimization, data analysis, and machine learning.

5. How are non-inner product metric spaces used in real-world applications?

Non-inner product metric spaces are used in a variety of real-world applications, such as computer vision, natural language processing, and pattern recognition. They are also important in physics and engineering for understanding and modeling complex systems. Additionally, non-inner product metric spaces are used in the study of quantum mechanics and general relativity, where traditional inner product spaces do not apply.

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