Linear Algebra Unique Vectors Help

As for part B, you have to show that there is a unique solution. It may be possible to show the solution is unique by using the fact that the columns of A are orthogonal, and so the columns of ##A^T A## are linearly independent. I haven't tried to fill in the details.
  • #1
rusty_shakle
1
0
1. (A)Homework Statement
Let the following be A=
[itex]\left|-1/\sqrt{6} ... 1/\sqrt{3}\right|[/itex]
[itex]\left|1/\sqrt{6}... -1/\sqrt{3}\right|[/itex]
[itex]\left|2/\sqrt{6}... 1/\sqrt{3}\right|[/itex]

***excuse the "..." on the matrix, I didn't know how to space them out so I used dots instead***

And the other B=
[itex]\left|1\right|[/itex]
[itex]\left|1\right|[/itex]
[itex]\left|0\right|[/itex]

Find the unique vector p [itex]\in[/itex] R(A) such that
[itex]\left\|p-b\right\| < \left\|Ax-b\right\|[/itex]
for all x [itex]\in[/itex][itex]\Re ^2[/itex]


(B) Does there exist a vector x[itex]_{0}[/itex][itex]\in[/itex][itex]\Re ^2[/itex] such that Ax[itex]_{0}[/itex]=p? If so, is x[itex]_{0}[/itex] unique? Justify your answer.

Homework Equations



The Attempt at a Solution


For part A I basically combined the matrix, and added x1+x2 = B, I then solved for x1 and x2. Needless to say, I got it wrong. Am I going in the right direction for this problem? I'm not very good at abstract algebra and I'm not very sure what it's asking me.

Could someone please help me solve this problem in order to prepare for my final exam?
Thank you.
 
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  • #2
rusty_shakle said:
1. (A)Homework Statement
Let the following be A=
[itex]\left|-1/\sqrt{6} ... 1/\sqrt{3}\right|[/itex]
[itex]\left|1/\sqrt{6}... -1/\sqrt{3}\right|[/itex]
[itex]\left|2/\sqrt{6}... 1/\sqrt{3}\right|[/itex]

***excuse the "..." on the matrix, I didn't know how to space them out so I used dots instead***

And the other B=
[itex]\left|1\right|[/itex]
[itex]\left|1\right|[/itex]
[itex]\left|0\right|[/itex]

Find the unique vector p [itex]\in[/itex] R(A) such that
[itex]\left\|p-b\right\| < \left\|Ax-b\right\|[/itex]
for all x [itex]\in[/itex][itex]\Re ^2[/itex](B) Does there exist a vector x[itex]_{0}[/itex][itex]\in[/itex][itex]\Re ^2[/itex] such that Ax[itex]_{0}[/itex]=p? If so, is x[itex]_{0}[/itex] unique? Justify your answer.

Homework Equations



The Attempt at a Solution


For part A I basically combined the matrix, and added x1+x2 = B, I then solved for x1 and x2. Needless to say, I got it wrong. Am I going in the right direction for this problem? I'm not very good at abstract algebra and I'm not very sure what it's asking me.

Could someone please help me solve this problem in order to prepare for my final exam?
Thank you.
Here's your B matrix in a little nicer form. If you Quote this post you can see what I did.

$$A = \begin{bmatrix} -1/\sqrt{6} & 1/\sqrt{3} \\
1/\sqrt{6} & -1/\sqrt{3} \\
2/\sqrt{6} & 1/\sqrt{3}\end{bmatrix} $$

Your vector b is
$$\begin{bmatrix} 1 \\ 1 \\ 0 \end{bmatrix}$$

I don't have this figured out, but I'll share what I've found. I'm thinking in terms of the geometry here. The columns in B are orthogonal and have magnitude 1, which makes them orthonormal. The expression Ax could also be written as
$$ x_1\begin{bmatrix}-1/\sqrt{6}\\ 1/\sqrt{6} \\ 2/\sqrt{6}\end{bmatrix} + x_2\begin{bmatrix} 1/\sqrt{3}\\ -1/\sqrt{3} \\ 1/\sqrt{3}\end{bmatrix}$$

The above is a linear combination of the columns of A, which defines a plane in R3. You could find the normal to this plane by taking the cross product of the two vectors above. Note that you don't have enough information to find the equation of this plane, because you don't have a point in the plane.

The expression Ax - b, or Ax + -b, can be interpreted as meaning "go out from the origin in the direction of -b, and then go to a point in the plane as specified by the coordinates (x1, x2), where these coordinates are relative to the two column vectors in A.

If the plane that I have described happens to go through the origin, then the minimum value of ##\left\|Ax-b\right\| ## is zero. If the plane as described doesn't go through the origin, then you are basically finding the point on the plane that is closest to the origin.
 
  • #3
You are asked to find ##x## which minimizes ##\|Ax - b\|##. This is equivalent to minimizing ##\|Ax - b\|^2##. Assuming the norm is the usual euclidean 2-norm, we have
$$\|Ax - b\|^2 = (Ax - b)^T (Ax - b) = x^T A^T A x - 2b^T A x + b^T b$$
I assume that somewhere in your course you must have covered how to minimize such an expression. One way to do this is to differentiate with respect to ##x##.

Another way is to use the following geometric insight: in order to make ##Ax## as close to ##b## as possible, the error vector ##Ax - b## needs to be orthogonal to ##Ax##. (Draw a triangle to see why.) In other words, you need to find ##x## such that
$$(Ax - b)^T Ax = 0$$
But again, I'm assuming this must be covered in your notes somewhere. It would be challenging to solve this sort of problem from scratch without having seen the method (least squares) at some point.
 
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FAQ: Linear Algebra Unique Vectors Help

What is a unique vector in linear algebra?

A unique vector in linear algebra is a vector that has a distinct direction and magnitude from any other vector in the same vector space. It is not a linear combination of other vectors and cannot be written as a multiple of another vector. Unique vectors are important in linear algebra as they provide a basis for creating other vectors and determining the dimensions of a vector space.

How is the uniqueness of a vector determined?

The uniqueness of a vector is determined by its components or coordinates. If two vectors have the same components, they are not unique. However, if the components of two vectors are different, they are unique and have distinct directions and magnitudes.

Can a vector be unique in one vector space and not in another?

Yes, a vector can be unique in one vector space and not in another. The uniqueness of a vector is dependent on the basis of the vector space. If the basis is different, the components of the vector will be different and thus the vector will be unique in one vector space but not in another.

How do unique vectors relate to linear independence?

Unique vectors are linearly independent. This means that no unique vector can be written as a linear combination of other unique vectors in the same vector space. In other words, unique vectors are the building blocks of linearly independent sets in linear algebra.

What is the importance of unique vectors in linear algebra applications?

Unique vectors are important in linear algebra applications because they help determine the dimensions of a vector space, provide a basis for creating other vectors, and are used in solving systems of linear equations. They also play a crucial role in understanding linear transformations and their properties.

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