Image of a Matrix and symmetric matrix

In summary, the image of a matrix is the set of possible outputs from a linear transformation represented by that matrix. It is not calculated by Lx = x, but rather by finding all vectors in the range that can be obtained by applying the transformation to vectors in the domain. The nullspace is the subspace of the domain that is mapped to the zero vector in the range. The product of two matrices is symmetric when it is equal to its transpose, and this can be determined by looking at the matrices themselves.
  • #1
Payam30
46
1
Hi,
Well I hope it's not a thread that already is in the storage here. I want to understand the image of a matrix. not only calculating it but also why I'm doing that. Here are my questions:

1) They say d = Lx has a solution if d ∈ ImL. I know that the image of a matrix is calculated by Lx = x. but here we have d = Lx.

2) Can anybody show me what image of a matrix and what solution the question above has geometrically? I mean by firgures?

3) If A is a nxn matrix and B is a nx1 matrix and you only have the image of A , what does it say above the image of Γ = [B, AB]?

4) If We know the image of A and B where both A and B are nxn. what do you know about the image of C=AB , C = A+B?

and last question which doesn't have so much to do with image:
5) When is the product of two matrices symmetric? by only looking at them?

I appritiate all kind of help. thanks in advance
 
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  • #2
The image (range, column space) of a matrix is not calculated by ##L\mathbf x= \mathbf x##, check your source.
 
  • #3
I am not familiar with your terminology, but I will make some statements that I hope are relevant.
  1. A matrix is a representation of a linear operator in ℝn
  2. Therefore matrices obeys the rules for linear operators - if A is a matrix, X and Y are points in ℝn and α, β are real numbers, then A(αX+βY) = αAX + βAY.
  3. Matrix multiplication is defined for certain combinations of matrices - if we have Am,p and Bp,n, then Cm,n = A⋅B exists.
  4. Matrix multiplication is not commutative - in general A⋅B ≠ B⋅A
A linear operator is symmetric with respect to an inner product <,> if <AX,y> = <x,Ay>
 
  • #4
Payam30 said:
Hi,
Well I hope it's not a thread that already is in the storage here. I want to understand the image of a matrix. not only calculating it but also why I'm doing that.
A matrix is a representation of a linear transformation, which you can think of as a kind of function. The image of a linear transformation is the set of possible outputs from that transformation.

In another reply, Svein called this a linear operator, but as I recall things, a linear operator is a transformation (or mapping) from a given vector space to itself. Hence any matrix for a linear operator has to be square. For a linear transformation, the dimensions of the domain and range don't have to be the same. Hence, a matrix for such a linear transformation can by m x n, where m and n aren't equal. In this case, the linear transformation maps vectors in Rn (the domain) to vectors in Rm (the range).
Payam30 said:
Here are my questions:

1) They say d = Lx has a solution if d ∈ ImL. I know that the image of a matrix is calculated by Lx = x. but here we have d = Lx.
The image is not calculated by Lx = x. A vector y is in the image of L -- Im(L) -- if and only if there exists a vector x in the domain of L such that Lx = y.
Payam30 said:
2) Can anybody show me what image of a matrix and what solution the question above has geometrically? I mean by firgures?
Here's a simple example where L is a transformation of R2 to R2, with this matrix:
$$A = \begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix}$$
A maps a vector <x, y> to <y, 0>. (Check this for yourself.) Geometrically, A reflects an input vector across the line y = x, and then projects that vector onto the x-axis. These two steps transform <x, y> to <y, x> and then to <y, 0>. So for example,
$$A \begin{bmatrix}2 \\ 3 \end{bmatrix} = \begin{bmatrix} 0 & 1 \\ 0 & 0 \end{bmatrix} \begin{bmatrix}2 \\ 3 \end{bmatrix} = \begin{bmatrix}3 \\ 0 \end{bmatrix}$$

Another piece of information that is related to the image of a transformation is its nullspace, the subspace of the domain that is mapped to the zero vector in the range. A vector x is in the nullspace of L iff Lx = 0. As it turns out, the nullspace of the matrix of my example is the set of vectors in R2 whose y-component is 0; in other words, any vector that lies along the x-axis.
Payam30 said:
3) If A is a nxn matrix and B is a nx1 matrix and you only have the image of A , what does it say above the image of Γ = [B, AB]?
I don't know what this notation means.
Payam30 said:
4) If We know the image of A and B where both A and B are nxn. what do you know about the image of C=AB , C = A+B?

and last question which doesn't have so much to do with image:
5) When is the product of two matrices symmetric? by only looking at them?

I appritiate all kind of help. thanks in advance
 

Related to Image of a Matrix and symmetric matrix

1. What is an image of a matrix?

An image of a matrix is the set of all possible outputs that can be obtained by multiplying the matrix with different input vectors. It represents the range of the linear transformation defined by the matrix.

2. How is the image of a matrix related to the dimensions of the matrix?

The image of a matrix is a subset of the vector space defined by the dimensions of the matrix. The dimension of the image cannot be greater than the number of rows in the matrix, and it is equal to the rank of the matrix.

3. What is a symmetric matrix?

A symmetric matrix is a square matrix that is equal to its transpose. In other words, it is a matrix that is unchanged when its rows and columns are interchanged.

4. How can you determine if a matrix is symmetric?

A matrix is symmetric if it is equal to its transpose. This means that the element at the i-th row and j-th column is equal to the element at the j-th row and i-th column for all i and j. Another way to determine if a matrix is symmetric is by checking if it is equal to its own inverse.

5. What are the properties of a symmetric matrix?

Some properties of a symmetric matrix include:

  • The diagonal elements are always real numbers.
  • The eigenvalues are always real numbers.
  • The eigenvectors corresponding to distinct eigenvalues are orthogonal.
  • The sum of two symmetric matrices is also a symmetric matrix.

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