Finding basis for kernal of linear map

[PhyStan7]

Homework Statement

Let A = 1 3 2 2
1 1 0 -2
0 1 1 2

Viewing A as a linear map from M_(4x1) to M_(3x1) find a basis for the kernal of A and verify directly that these basis vectors are indeed linearly independent.

The Attempt at a Solution

Ok so first i found the reduced row echelon form of A. This equals:

rref(A) =

1 0 -1 -4
0 1 1 2
0 0 0 0

So i found the kernal of this by-

1 0 -1 -4
0 1 1 2
0 0 0 0

Multiplied by

x_1
x_2
x_3
x_4

Equals

0
0
0
0.



x_1 = x_3 + x_4
x_2 = -x_3-2x_4
x_3 = x_3
x_4 = x_4

Therefore kernal...

=x_3 {1, -1, 1, 0} + x_4 {1, -2, 0, 1}

So i thought this meant the basis equalled

Basis of kernal = (1,-1,1,0),(1,-2,0,1)


I have idea what to do now though. I have no idea if what i have done is vaguely right and am not sure if it is how to fulfill the rest of the question. The problem is i have not really incoperated the fact that in the question it states that Viewing A as a linear map from M_(4x1) to M_(3x1). I do not understand this terminology, what does it mean exactly?

(ps - i appologise for the bad formatting)


Thanks

 

[tiny-tim]

Hi PhyStan7!

(try using the X₂ tag just above the Reply box )

… So i found the kernal of this by-

1 0 -1 -4
0 1 1 2
0 0 0 0

Multiplied by

x_1
x_2
x_3
x_4

Equals

0
0
0
0.



x_1 = x_3 + x_4

Nooo … 4x₄

I have idea what to do now though. I have no idea if what i have done is vaguely right and am not sure if it is how to fulfill therest of the question. The problem is i have not really incoperated the fact that in the question it states that Viewing A as a linear map from M_(4x1) to M_(3x1). I do not understand this terminology, what does it mean exactly?

I assume M_4x1 is the 4x1 matrices or column vectors.

So A is a function from the 4-column vectors to the 3-column vectors.

Any (constant) matrix is linear, so it's a linear function (linear map).

Only functions (maps) have kernels, so you have to view the matrix as a map to talk about a kernel.

 

[HallsofIvy]

You want to solve

\begin{bmatrix}1 & 3 & 2 & 2 \\ 1 & 1 & 0 & -2 \\ 0 & 1 & 1 & 1\end{bmatrix}\begin{bmatrix}w \\ x \\ y \\ z\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0\end{bmatrix}.



Which is the same as the three equations w+ 3x+ 2y+ 2z= 0, w+ x- 2z= 0, x +y+ z= 0.

Adding the first two equations eliminates z: 2w+ 4x+ 2y= 0. Multiplying the third equation by 2 and adding to the second equation also eliminates z: w+ 3x+ 2y= 0.

Subtracting the second of those from the first eliminaes y: w+ x= 0 so x= -w.

Putting that back into the previous equations will allow you to write each of x, y, and z in terms of w. The kernel is one-dimensional, not two-dimensional.

A "linear map" is a "linear" function from one vector space to another. If f is a linear map then f(au+ bv)= af(u)+ bf(v) fpr any vectors u and v in the domain, any scalars a and b.

You can think of an "m by n" matrix as a linear map from R^m to R^n. Conversely, any linear map from from m-dimensional U to n dimensional V can be written as an m by n matrix for specific bases for U and V.