Using rank-nullity theorem to show alternating sum of dimensions = 0

In summary, the conversation is about using the rank-nullity theorem to show that the sum of alternating terms in an integer sequence is equal to zero. The theorem states that the dimensions of the image and kernel of a linear map are equal to the dimension of the vector space it is mapping from. By setting up a system of equations and using the relation between the images and kernels of the matrices involved, the answer can be derived.
  • #1
bmanbs2
22
0

Homework Statement



Consider integer sequence [tex]n_{1},...,n_{r}[/tex] and matrices [tex]A_{1},...,A_{n-1}[/tex]. Assume [tex]im\left(A_{i}\right) = ker\left(A_{i+1}\right)[/tex]

Using the rank-nullity theorem, show that [tex]\sum^{n}_{i=1}\left(-1\right)^{i}d_{i} = 0[/tex]


Homework Equations



The rank-nullity theorem states that if [tex]v[/tex] and [tex]w[/tex] are vector spaces and [tex]A[/tex] is the linear map A: v -> w, then
[tex]dim\left(im\left(A\right)\right) + dim\left(ker\left(A\right)\right) = dim\left(v\right)[/tex]

The Attempt at a Solution



I know that the relation [tex]im\left(A_{i}\right) = ker\left(A_{i+1}\right)[/tex] means that [tex]\sum^{n}_{i even}d_{i}[/tex] = [tex]\sum^{n}_{i odd}d_{i}[/tex], but I don't know how to get there.
 
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  • #2
First, apply rank-nullity on A1,...,An. You'll get n equations. Adding up those equations should get you the answer...
 
  • #3
Thanks for the reply, but I want to make sure I'm setting up the equations properly.

So far I have that
[tex]im\left(A_{i-1}\right) + im\left(A_{i}\right) = d_{i}[/tex]
[tex]im\left(A_{i}\right) + im\left(A_{i+1}\right) = d_{i+1}[/tex]

Subtracting the system of equations gives
[tex]im\left(A_{i-1}\right) - im\left(A_{i+1}\right) = d_{i} - d_{i+1}[/tex]

That may then be continued for [tex]2 \leq i \leq n-1[/tex], but I'm not sure how to make it all equal zero.
 
  • #4
OK, I was wrong about adding up the equations. But what you have looks good:

[tex]im(A_{i-1})+im(A_i)=d_i[/tex].

Now, you have to calculate

[tex]-d_1+d_2-d_3+d_4-...[/tex]

Just substitute every di in the above addition by [tex]im(A_{i-1})+im(A_i)[/tex], and make a little calculation.
 

1. How is the rank-nullity theorem used to show that the alternating sum of dimensions is equal to 0?

The rank-nullity theorem states that for a linear transformation, the sum of the dimensions of the null space (ker) and the range (im) is equal to the dimension of the domain. In the case of an alternating sum of dimensions, we can use this theorem to show that the dimensions cancel each other out, resulting in a sum of 0.

2. Can the rank-nullity theorem be applied to any linear transformation?

Yes, the rank-nullity theorem can be applied to any linear transformation, as long as the transformation is defined on a finite-dimensional vector space.

3. What is the significance of the alternating sum of dimensions being equal to 0?

The alternating sum of dimensions being equal to 0 indicates that the linear transformation preserves the dimensionality of the vector space. This can be useful in understanding the structure and properties of the linear transformation.

4. Are there any exceptions to the rank-nullity theorem when applied to alternating sums of dimensions?

No, there are no exceptions to the rank-nullity theorem when applied to alternating sums of dimensions. This theorem holds true for all linear transformations on finite-dimensional vector spaces.

5. How can the rank-nullity theorem be used in real-world applications?

The rank-nullity theorem has many applications in fields such as computer science, engineering, and economics. It can be used to understand the dimensionality of data sets, analyze the efficiency of algorithms, and determine the solvability of systems of linear equations.

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