Linear transformation of matrices

Linear transformation of matrices are linear operations using matrices that modify the initial dimension of a given vector.

In other words, we can modify the dimension of a vector by multiplying it by any matrix.    

Linear transformations are the basis of the vectors and eigenvalues ​​of a matrix since they depend linearly on each other. 

Recommended articles: operations with matrices , vectors and eigenvalues . 


We define any matrix  C of dimension 3 × 2 multiplied by a vector V of dimension  n = 2 such that V = (v 1 , v 2 ). 

Linear transformation of matrices

What dimension will the result vector be?

The vector resulting from the product of the matrix  C 3 × 2 with the vector  2 × 1 will be a new vector V ‘of dimension 3.  

Product Result Vector

This change in dimension of the vector is due to the linear transformation by the matrix  C .

Practical example 

Given the square matrix  R with dimension 2 × 2 and the vector  V of dimension 2.

Matrix And Vector

A linear transformation of the dimension of vector  V is: 

Linear Transformation Solved

where the initial dimension of vector  V was 2 × 1 and now the final dimension of vector  V is 3 × 1. This change in dimension is achieved by matrix multiplication  R .

Can these linear transformations be represented graphically? Well of course!

We will represent the result vector V ‘on a plane. 


V = (2.1) 

V ‘= (6.4)


Linear transformation graph

Eigenvectors by graphical representation

How can we determine that a vector is an eigenvector of a given matrix just by looking at the graph?

We define the 2 × 2 dimension matrix  D : 

2x2 matrix 1

Are the vectors v 1 = (1,0) and v 2 = (2,4) eigenvectors of the matrix D ?


1. Let’s start with the first vector v 1 . We do the above linear transformation: 

First Vector

So if vector v 1 is actually eigenvector of matrix D , the resulting vector v 1 ‘and vector v 1 should belong to the same line.

We represent v  = (1,0) and v 1 ‘= (3,0). 

Vector representation

Since both v 1 and v 1 ‘belong to the same line, v 1 is an eigenvector of the matrix  D .

Mathematically, there is a constant  h (eigenvalue) such that: 

Constant Own Vector

2. We continue with the second vector v 2 . We repeat the above linear transformation: 

Second Vector

So, if vector v 2 is really eigenvector of matrix D , the resulting vector v 2 ‘and vector v 2 should belong to the same line (like the previous graph). 

We represent v  = (2,4) and v 2 ‘= (2,24). 

Vector Rendering 2

Since v 2 v 2 ‘non Similarly, v 2 is an eigenvector of the matrix  D . 

Mathematically, there is no constant  h (eigenvalue) such that: 

There is no constant

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