A and solution are known find B matrix

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Discussion Overview

The discussion revolves around the possibility of finding a matrix B such that the product of matrix A and B equals a specified matrix. The context includes exploring the implications of matrix A being singular and the methods for solving underdetermined systems of equations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant asserts that since matrix A is singular, it cannot be inverted, implying that no matrix B can satisfy the equation AB = C.
  • Another participant counters that the system is underdetermined, suggesting that there are infinitely many solutions for B, and provides specific solutions derived from Gaussian elimination.
  • Further elaboration includes the use of the Moore-Penrose pseudoinverse to find a least-squares solution for B.
  • A question is raised regarding the derivation of specific values for B31 and B32, seeking clarification on whether these were obtained through Gaussian elimination or another method.
  • Another participant explains the Gaussian elimination process step-by-step, demonstrating how to derive one of the infinite solutions and suggesting alternative starting values for variables to find different solutions.

Areas of Agreement / Disagreement

Participants express differing views on the implications of matrix A being singular. While one participant believes this precludes the existence of a solution, others argue that multiple solutions do exist, leading to an unresolved debate on the interpretation of singular matrices in this context.

Contextual Notes

The discussion highlights the limitations of the singularity of matrix A and the dependence on the methods used for solving the equations. The existence of multiple solutions is acknowledged, but the exact nature of these solutions and the methods to derive them remain a point of contention.

TomSavage
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I have the matrix of A

1 2 -1
2 -1 1

and i am asked if there is any B matrix that can make AB = 1-1
1 1

I assume that this is not possible because if we follow the law of Ax=B then {A}^{-1} * B =x and since matrix a is singular then it cannot be inverted and thus this operation is impossible. Am I wrong in thinking this?
 
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TomSavage said:
I have the matrix of A

1 2 -1
2 -1 1

and i am asked if there is any B matrix that can make AB = 1-1
1 1

I assume that this is not possible because if we follow the law of Ax=B then {A}^{-1} * B =x and since matrix a is singular then it cannot be inverted and thus this operation is impossible. Am I wrong in thinking this?

Hi TomSavage! Welcome to MHB! ;)

I'm afraid that's not quite true.

We can solve
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
b_{11}\\b_{21}\\b_{31}
\end{pmatrix} = \begin{pmatrix}
1\\1
\end{pmatrix}$$
can't we?

It is under determined so that it has infinitely many solutions.
We can solve it with Gaussian elimination and pick for instance:
$$\begin{pmatrix}
b_{11}\\b_{21}\\b_{31}
\end{pmatrix} = \begin{pmatrix}
3/5\\1/5\\0
\end{pmatrix}$$

Similarly we can solve:
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
b_{12}\\b_{22}\\b_{32}
\end{pmatrix} = \begin{pmatrix}
-1\\1
\end{pmatrix}$$

Combining the result with the first solution we get:
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
3/5 &0 \\1/5 &0 \\0 & 1
\end{pmatrix} = \begin{pmatrix}
1 & -1\\1 & 1
\end{pmatrix}$$

There you go, a solution for $B$.

More generally, we can find a solution in the least-square sense with:
$$B=A^+ \begin{pmatrix}
1 & -1\\1 & 1
\end{pmatrix}$$
where $A^+$ is the Moore-Penrose pseudoinverse.
The wiki article also explains how we can use it to find all solutions.
 
Klaas van Aarsen said:
Hi TomSavage! Welcome to MHB! ;)

I'm afraid that's not quite true.

We can solve
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
b_{11}\\b_{21}\\b_{31}
\end{pmatrix} = \begin{pmatrix}
1\\1
\end{pmatrix}$$
can't we?

It is under determined so that it has infinitely many solutions.
We can solve it with Gaussian elimination and pick for instance:
$$\begin{pmatrix}
b_{11}\\b_{21}\\b_{31}
\end{pmatrix} = \begin{pmatrix}
3/5\\1/5\\0
\end{pmatrix}$$

Similarly we can solve:
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
b_{12}\\b_{22}\\b_{32}
\end{pmatrix} = \begin{pmatrix}
-1\\1
\end{pmatrix}$$

Combining the result with the first solution we get:
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
3/5 &0 \\1/5 &0 \\0 & 1
\end{pmatrix} = \begin{pmatrix}
1 & -1\\1 & 1
\end{pmatrix}$$

There you go, a solution for $B$.

More generally, we can find a solution in the least-square sense with:
$$B=A^+ \begin{pmatrix}
1 & -1\\1 & 1
\end{pmatrix}$$
where $A^+$ is the Moore-Penrose pseudoinverse.
The wiki article also explains how we can use it to find all solutions.
Hey, I now understand mostly everything but what I don't know is how you got the solutions for B31 and B32. Did you get the other 4 solutions from gauss jordan and then just put the other two in that satisfy the solution or is there a direct mathematical method to get them because wouldn't adding the (3X1) solution set into the (2x3) matrix mess things up?
 
TomSavage said:
Hey, I now understand mostly everything but what I don't know is how you got the solutions for B31 and B32. Did you get the other 4 solutions from gauss jordan and then just put the other two in that satisfy the solution or is there a direct mathematical method to get them because wouldn't adding the (3X1) solution set into the (2x3) matrix mess things up?

Let me show you how to solve:
$$\begin{pmatrix}
1 &2 &-1\\
2 &-1 &1
\end{pmatrix}\begin{pmatrix}
b_{11}\\b_{21}\\b_{31}
\end{pmatrix} = \begin{pmatrix}
1\\1
\end{pmatrix}$$
with Gaussian elimination.

Take the first row, multiply it with $-2$, and add it to the second row.
Consequently we get a new system that has the same solutions as the previous system.
The result is:
$$\begin{pmatrix}
1 &2 &-1\\
0 &-5 &3
\end{pmatrix}\begin{pmatrix}
b_{11}\\b_{21}\\b_{31}
\end{pmatrix} = \begin{pmatrix}
1\\-1
\end{pmatrix}$$

Starting from the bottom, we pick $b_{31}=0$, and see what happens afterwards.
To solve the second equation, we need $b_{21}=\frac 15$ now.
Then we solve the first equation, using the values we found so far, and we can with $b_{11}=\frac 35$.

There you go. We found one of the infinite number of solutions: $(b_{11},b_{21},b_{31})=(\frac 35, \frac 15, 0)$.Alternatively, we might have started with $b_{21}=0$.
Then we need $b_{31}=-\frac 13$ to solve the second equation.
And finally $b_{11}=\frac 23$ to solve the first equation for the solution $(b_{11},b_{21},b_{31})=(\frac 23, 0, -\frac 13)$.
Note that these 2 solutions together 'span' the solution space.
 

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