The VERY last part of finding an eigenvector

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In summary, there is no single "eigenvector" corresponding to a given eigenvalue. The set of all eigenvectors corresponding to a given eigenvalue is a subspace, and any multiple of an eigenvector is also an eigenvector. This means that in the case of ax = by, the vector can be represented by any of the following: (a/b, 1), (1, b/a), (a, b), or (a/\sqrt{a^2+ b^2}, b/\sqrt{a^2+ b^2}). The important thing is that they all represent the same direction or subspace, with different magnitudes.
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claret_n_blue
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I can do everything, until I get to the point of actually putting the system of equations into that eigenvector "form". I won't use my actual numbers, but say I have solved everything and got:

ax = by
ax = by

Where a and b are 2 numbers, but a doesn't equal b.

Does this mean that for everyone 1 'y', I get (a/b) lots of x's, and so my vector is (a/b, 1). Or is it the other way round so for everyone 1 'x', I get (b/a) y's, so my vector is (1,b/a). Or is it simply (a,b)?

Thank you for your help :)
 
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  • #2
Oops, you got your algebra slightly wrong. It should be (b/a) lots of x's for every 1 y, etc.

The eigenvector just defines a "direction", not a magnitude, so you might as well use the simplest form you can write down. That would be (b,a) for your example.

For some applications of eigenvectors, the magnitude is "normalized", for example so that xTx = 1, or xTAx = 1 where A is some matrix, or a particular element in the vector equals 1. But there is no point in doing that unless you have a reason for doing it.
 
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  • #3
claret_n_blue said:
I can do everything, until I get to the point of actually putting the system of equations into that eigenvector "form". I won't use my actual numbers, but say I have solved everything and got:

ax = by
ax = by

Where a and b are 2 numbers, but a doesn't equal b.

Does this mean that for everyone 1 'y', I get (a/b) lots of x's, and so my vector is (a/b, 1). Or is it the other way round so for everyone 1 'x', I get (b/a) y's, so my vector is (1,b/a). Or is it simply (a,b)?

Thank you for your help :)
How about "all of the above"? If you have a single equation, ax= by, then there are an infinite number of (x, y) pairs that satisfy the equation. (a/b, 1), (1, b/a), and (a, b) are all among them. Those all represent vectors in the same direction, or in the same subspace, with different lengths. So is another important pair: [itex](a/\sqrt{a^2+ b^2}, b/\sqrt{a^2+ b^2})[/itex], the unit vector in that direction.

The crucial point is this- there is no single "eigenvector" corresponding to a given eigenvalue. The set of all eigenvectors corresponding to a given eigenvalue is a subspace. In particular, any multiple of an eigenvector is also an eigenvector. (a/b, 1)= (1/b)(a, b), (1, b/a)= 1/a(1, b), and [itex](a/\sqrt{a^2+ b^2}, b/\sqrt{a^2+ b^2})= [1/\sqrt{a^2+ b^2}](a, b)[/itex] are all multiples of (a, b).
 

1. What is an eigenvector?

An eigenvector is a vector that does not change its direction when multiplied by a particular square matrix. It is associated with a scalar value called an eigenvalue and is an important concept in linear algebra.

2. Why is finding the eigenvector important?

Finding the eigenvector is important because it helps to understand the behavior of linear transformations, such as rotations and stretches, and can be used to simplify complex calculations in various fields of science and engineering.

3. How is the eigenvector calculated?

The eigenvector is calculated by solving a system of linear equations, where the coefficients are the elements of the square matrix and the unknown variables are the elements of the eigenvector. This is typically done using methods such as Gaussian elimination or the eigenvalue decomposition.

4. What is the relationship between eigenvectors and eigenvalues?

Eigenvectors and eigenvalues are closely related, as each eigenvector is associated with a specific eigenvalue. The eigenvalue represents the scalar value by which the eigenvector is stretched or compressed when multiplied by the square matrix.

5. How is the eigenvector used in real-world applications?

The eigenvector has many real-world applications, such as in image and signal processing, physics, and economics. It can be used to analyze the stability of systems, identify patterns and trends in data, and reduce the dimensionality of complex problems.

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