Vector Space of Matrices: How to Define and Illustrate?

In summary, the conversation discusses the example of "The space of functions from a set S to a field F" and the lack of detail given in linear algebra texts. The speaker then defines the set as a vector space with specific operations and notation. They also mention the possibility of illustrating a vector space of matrices using similar notation. The conversation ends with a clarification on the definition of a set and the speaker's explanation of their use of language.
  • #1
sponsoredwalk
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You know the example "The space of functions from a set S to a field F" that's usually
given in a linear algebra text? Well they never give an example of the set they're working in
in detail so I defined the set as:

((S, (S x S, S, +)), ((F, (F x F, F, +')), (F x F, F, °)), (S x F, F, •))

where:

+ : S x S → S defined by + : (f,g) ↦ (f + g)(x) = f(x) + g(x)
• : S x F → F defined by • : (f,β) ↦ (βf)(x) = βf(x).

&

(S, (S x S, S, +)) is an abelian group of vectors (functions),
((F, (F x F, F, +')), (F x F, F, °)) is the field over which the operations take place,
(S x F, F, •) is the operation of scalar multiplication on vectors (functions).

My questions is: How would I illustrate a vector space of matrices akin to the notation
above?


Since a matrix is just the function f : (i,j) ↦ A(i,j) = Aij (as defined in Hoffman/Kunze anyway!).
I think the function is more generally defined as
f : Fm x n x Fm x n → Fm x n

To translate it into the above language I'm thinking:

((Fm x n, (Fm x n x Fm x n, Fm x n, +)), ((F, (F x F, F, +')), (F x F, F, °)), (Fm x n x F, Fm x n, •))

where

+ : Fm x n x Fm x n → Fm x n defined by + : (i,j) ↦ (A + B)(i,j) = A(i,j) + B(i,j) = Aij + Bij

• : Fm x n x F → Fm x n defined by • : ((i,j),β) ↦ (βA)(i,j) = βA(i,j) = βAij

But that seems weird tbh, is it correct?
 
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  • #2
? You said you were going to define a set but what you define is not simply a set. A set would be something like "{a, b, c}". What would be the set of functions from that to, say, the field fo rational numbers?
 
  • #3
I'm just going by the guidelines I've been given in this post:

https://www.physicsforums.com/showpost.php?p=3105066&postcount=7

Maybe the language was a bit loose, re-read the sentence:

"Well they never give an example of the set they're working in in detail so I defined the set as:"

as:

"Well they never give an example of the sets they're working in in detail so I defined the vector space as:"


and hopefully it will make more sense.
 

What is a vector space of matrices?

A vector space of matrices is a mathematical concept that describes a set of matrices that follow certain rules and properties. These properties include closure under addition and scalar multiplication, associativity, and the existence of an identity element.

How is a vector space of matrices different from a regular vector space?

A regular vector space consists of vectors, which are one-dimensional arrays of numbers. In contrast, a vector space of matrices consists of matrices, which are two-dimensional arrays of numbers. Additionally, the operations of addition and multiplication in a vector space of matrices are defined differently compared to a regular vector space.

What are some examples of vector spaces of matrices?

Some examples of vector spaces of matrices include the space of 2x2 matrices, the space of symmetric matrices, and the space of diagonal matrices. These spaces follow the properties of a vector space of matrices and contain matrices as their elements.

What is the importance of vector spaces of matrices in mathematics?

Vector spaces of matrices are important in many areas of mathematics, including linear algebra, functional analysis, and differential equations. They provide a framework for understanding and solving complex systems of equations and can be applied to various real-world problems in physics, engineering, and computer science.

How are vector spaces of matrices used in data analysis and machine learning?

In data analysis and machine learning, vector spaces of matrices are used to represent and manipulate data sets. They can be used to perform operations such as data transformation, dimensionality reduction, and clustering. Additionally, concepts from vector spaces of matrices, such as eigenvectors and eigenvalues, are used in algorithms for data analysis and machine learning.

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