Exploring the Solvability of a Matrix Equation

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In summary, the conversation discusses a system of matrix equations involving square matrices with real elements and vectors in n-dimensional space. The solvability condition for the system is that the matrix A_1 must be singular and the vector x_1 must be orthogonal to the image of A_1. The conversation also considers the possibility of symmetric matrices and concludes that there are no symmetry conditions on the matrices involved. The conversation ends with the discovery of a solution to the system.
  • #1
Hootenanny
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I have a quick question regarding matrix equations. Usually, I would look this up but unfortunately I'm away from the office and library and it can't wait until I get back.

Let [itex]A_1[/itex] and [itex]A_2[/itex] be [itex]n\times n[/itex] square matrices with real elements and let [itex]\boldsymbol{x}_1\;,\boldsymbol{x}_2\in\mathbb{R}^n[/itex]. Further, let [itex]A_1 \boldsymbol{x}_1 = \boldsymbol{0}[/itex]. What is the solvability condition for the following system?

[tex]A_1\boldsymbol{x}_2 = A_2\boldsymbol{x}_1[/tex]

The result would suggest [itex]\boldsymbol{x}_1^\text{T}A_2\boldsymbol{x}_1 = 0[/itex], but I'm clearly missing something. I fairly certain its something minor that I just can't see.

Any help would be very much appreciated.
 
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  • #2
Can any of the x_{i} or A_{i} be inverted? (i.e., do you know anything about their determinants?)
 
  • #3
kdbnlin78 said:
Can any of the x_{i} or A_{i} be inverted? (i.e., do you know anything about their determinants?)
[itex]\boldsymbol{x}_i[/itex] are vectors in [itex]\mathbb{R}^n[/itex], and [itex]A_i[/itex] are singular in general.
 
  • #4
Apologies, can now see the x_{i} are vectors. I'm at work and scanning articles when no-one is looking.

I your reasoning has lead to to conclude that /boldsymbol{x_{1}^T}A_{1} = /boldsymbol{0} - How do you know this?
 
  • #5
kdbnlin78 said:
Apologies, can now see the x_{i} are vectors. I'm at work and scanning articles when no-one is looking.

I your reasoning has lead to to conclude that /boldsymbol{x_{1}^T}A_{1} = /boldsymbol{0} - How do you know this?
No problem :)

I'm tracing back a result and I've found that the result would only hold if the above relation is true.

I only asked because I assumed that solvability condition for an equation of the forum that I posted in my original post would be fairly well known, or at least established.
 
  • #6
Is your matrix symmetric by any chance?? In that case we have that

The thing is that

[tex]A_1x_2=A_2x_1[/tex]

has a solution if and only if [itex]A_2x_1\in im(A_1)[/tex].
But we know that [itex]im(A_1)=ker(A_1^T)^\bot[/itex].
So the system has a solution if and only if [itex]A_2x_1\in ker(A_1^T)^\bot=ker(A_1)^\bot[/itex].

So it must hold that [itex]x_1^TA_1x_1=0[/itex]. I fear that this is not a sufficient condition in general...
 
  • #7
micromass said:
Is your matrix symmetric by any chance?? In that case we have that

The thing is that

[tex]A_1x_2=A_2x_1[/tex]

has a solution if and only if [itex]A_2x_1\in im(A_1)[/tex].
But we know that [itex]im(A_1)=ker(A_1^T)^\bot[/itex].
So the system has a solution if and only if [itex]A_2x_1\in ker(A_1^T)^\bot=ker(A_1)^\bot[/itex].

So it must hold that [itex]x_1^TA_1x_1=0[/itex]. I fear that this is not a sufficient condition in general...
That was my first thought as well. Alas, there are no symmetry conditions on the matrices [itex]A_i[/itex].
 
  • #8
Nevermind - I've figure it out :D
 
  • #9
Hootenanny said:
Nevermind - I've figure it out :D

Can you tell us the solution?? :smile:
 

1. What is a matrix equation?

A matrix equation is an equation that involves matrices and their elements. It is written in the form of Ax = b, where A is a matrix, x is a vector, and b is a vector. The goal of solving a matrix equation is to find the value of x that satisfies the equation.

2. How do you determine if a matrix equation is solvable?

A matrix equation is solvable if the number of unknown variables (represented by x) is equal to the number of equations in the system. This means that the number of columns in matrix A must be equal to the number of rows in vector x. If these conditions are met, the matrix equation is solvable.

3. What is the importance of exploring the solvability of a matrix equation?

Exploring the solvability of a matrix equation is important because it allows us to determine if a solution exists and if it is unique. This is crucial in many fields of science, such as physics and engineering, where matrix equations are used to represent real-world problems.

4. What methods can be used to solve a solvable matrix equation?

There are several methods that can be used to solve a solvable matrix equation, including Gaussian elimination, Gauss-Jordan elimination, and the inverse matrix method. These methods involve manipulating the matrix equation using elementary row operations to reduce it to an equivalent equation with a unique solution.

5. Can a matrix equation have more than one solution?

No, a solvable matrix equation can only have one unique solution. If a matrix equation has more than one solution, it is considered to be inconsistent or underdetermined, meaning that there are not enough equations to determine a unique solution. In some cases, a matrix equation may have no solution at all.

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