Quick doubt about linear application and its matrix

In summary, the conversation discusses finding the matrix ##M^{E,E}_f## for a given function ##f:\mathbb{R}^3\to \mathbb{R}^3## and canonical basis ##E=(e_1,e_2,e_3)##, using the given vectors and their corresponding images under the function. The attempt at a solution involves setting up a system of equations and solving for the images of the basis vectors, which results in a matrix with the images as columns. However, the book's solution has the images as rows, which may be incorrect.
  • #1
Felafel
171
0

Homework Statement


Let ##f:\mathbb{R}^3\to \mathbb{R}^3## such that ##v_1=(1,0,1) , v_2=(0,1,-1), v_3=(0,0,2)## and ##f(v_1)=(3,1,0), f(v_2)=(-1,0,2), f(v_3)=(0,2,0)##
find ##M^{E,E}_f## where ##E=(e_1,e_2,e_3)## is the canonical basis.


The Attempt at a Solution


i see
##v_1=e_1+e_3##
##v_2=e_2-e_3##
##v_3=2e_3##
thus
##f(e_1)+f(e_3)=(3,1,0)##
##f(e_2)-f(e_3)=(-1, 0 ,2)##
##2f(e_3)=(0,2,0)##
solving the system i get
##f(e_3)=(0,1,0)##
##f(e_1)=(3,0,0)##
##f(e_2)=(-1,1,2)##

and so I thought the matrix was:

(3 -1 0)
(0 1 1)
(0 2 0)

but according to my book the solution should be:
(3 0 0)
(-1 1 2)
(0 1 0)

why? shouldn't the image of the vector form the columns instead of the rows?
thank you in advance :)
 
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  • #2
Felafel said:

Homework Statement


Let ##f:\mathbb{R}^3\to \mathbb{R}^3## such that ##v_1=(1,0,1) , v_2=(0,1,-1), v_3=(0,0,2)## and ##f(v_1)=(3,1,0), f(v_2)=(-1,0,2), f(v_3)=(0,2,0)##
find ##M^{E,E}_f## where ##E=(e_1,e_2,e_3)## is the canonical basis.


The Attempt at a Solution


i see
##v_1=e_1+e_3##
##v_2=e_2-e_3##
##v_3=2e_3##
thus
##f(e_1)+f(e_3)=(3,1,0)##
##f(e_2)-f(e_3)=(-1, 0 ,2)##
##2f(e_3)=(0,2,0)##
solving the system i get
##f(e_3)=(0,1,0)##
##f(e_1)=(3,0,0)##
##f(e_2)=(-1,1,2)##

and so I thought the matrix was:

(3 -1 0)
(0 1 1)
(0 2 0)

but according to my book the solution should be:
(3 0 0)
(-1 1 2)
(0 1 0)

why? shouldn't the image of the vector form the columns instead of the rows?
thank you in advance :)

Your answer looks right assuming the vectors are column vectors. Perhaps the book is treating them as row vectors and multiplying the matrix on the right?
 
  • #3
i don't see why it would do that though.
it's just wrong, probably
thank you :)
 

FAQ: Quick doubt about linear application and its matrix

1. What is a linear application?

A linear application, also known as a linear transformation, is a function that maps a vector space to another vector space in a way that preserves the structure of the space. In other words, it is a function that takes in vectors and outputs other vectors, while also preserving properties such as linearity and proportionality.

2. How is a linear application represented mathematically?

A linear application can be represented using a matrix. The columns of the matrix represent the input vectors, and the rows represent the output vectors. The values in the matrix are determined by how the linear application transforms each input vector into an output vector.

3. What is the relationship between a linear application and its matrix?

The matrix of a linear application is essentially a numerical representation of the function. The entries in the matrix correspond to the coefficients that are used to transform the input vectors into output vectors. This means that if we know the matrix, we can easily apply the linear application to any vector.

4. How do you determine if a matrix represents a linear application?

A matrix represents a linear application if it satisfies the properties of linearity. This means that the matrix must be square (same number of rows and columns), and it must satisfy the conditions of additivity and homogeneity. Additivity means that adding two input vectors and then applying the matrix should give the same result as applying the matrix to each vector separately and then adding the results. Homogeneity means that multiplying an input vector by a constant and then applying the matrix should give the same result as multiplying the output vector by the same constant.

5. What are the applications of linear applications and matrices in science?

Linear applications and matrices have a wide range of applications in science, particularly in fields such as physics, engineering, and computer science. They are used to model and analyze systems, make predictions, and solve problems. For example, in physics, matrices are used to represent the transformations of vectors in three-dimensional space, while in computer science, they are used to solve systems of linear equations and perform operations on images and data.

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