The basis of n x n matrices with matrix multiplication

In summary, to generate all nxn matrices using matrix multiplication, addition, and multiplication by scalar, we can use a set of 2n matrices, consisting of 1s in either the row or column position and 0s elsewhere. This can also be written as a tensor product of column and row vectors. However, there is some debate over whether this set should have 2n or 2(n-1) matrices, with some sources suggesting that there may be a repeated matrix in the set.
  • #1
etnad179
11
0
Hi All,

I recently came across the interesting notion of constructing the minimal set of nxn matrices that can be used as a basis to generate all nxn matrices given that matrix multiplication, and addition and multiplication by scalar are allowed.

Is there a way to construct an explicit set of matrices that do this?

I'm stuck at the moment with the following thought process:

say [tex] A,B \in M[/tex] then [tex] C_{ij} = (A \times B )_{ij} = \sum_k A_{ik} B_{kj} [/tex]

Now since we only need one of the products in the sum k to be non-zero - we can pick the k to be some unique value say k=0. We now have the 2n matrices [tex] A_{i0} \& B_{0j} [/tex] for [tex] i,j=0,...,n [/tex] that generate general matrices C (with a 1 in the A_{i0} or B_{0j} component since can use scalar mult.)--- But I haven't used the addition properties, so this 2n is too large (?). There is one repeat 00 so is the total 2n-1?

This would be reasonable except in the book Lie Groups, Lie Algebras... by Gilmore it says (without reason) 2(n-1), and he uses it a few times - so I presume not a typo...

Thanks in advance!
 
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  • #2
To generate all ##n\times n## matrices, we need ##n^2## many of them. The easiest ones are ##E_{ij}## with a one at position ##(i,j)## and zero elsewhere. Now we can write each of them as ##E_{ij} = e_i \otimes e_j## where ##e_{k}## is a vector with a one at position ##k## and zeroes elsewhere.

The tensor product here is the matrix multiplication of a column vector with a row vector.
(see e.g. https://www.physicsforums.com/insights/what-is-a-tensor/ )
 

1. What is a matrix?

A matrix is a rectangular array of numbers, symbols, or expressions arranged in rows and columns. It is often used to represent a set of linear equations or to store and manipulate data in mathematics and science.

2. What is the basis of a matrix?

The basis of a matrix is the set of vectors that span its column space. It is the minimum number of linearly independent vectors needed to represent all possible combinations of the columns of the matrix.

3. What is the significance of n x n matrices?

N x n matrices are square matrices with the same number of rows and columns. They are significant because they can represent systems of linear equations with an equal number of variables and equations, making them useful for solving equations and performing other mathematical operations.

4. How is matrix multiplication defined?

Matrix multiplication is a mathematical operation where two matrices are multiplied together to create a new matrix. It is defined as the process of multiplying each element in a row of the first matrix by the corresponding element in a column of the second matrix, and then adding the products to get the corresponding element in the resulting matrix.

5. What is the relationship between matrix multiplication and the basis of n x n matrices?

The basis of n x n matrices is closely related to matrix multiplication as it helps to determine the dimensions and properties of the resulting matrix. In particular, the number of linearly independent rows and columns in the resulting matrix is equal to the rank of the matrices being multiplied. Additionally, the basis vectors of the resulting matrix are linear combinations of the basis vectors of the matrices being multiplied.

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