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Juggler123
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Can zero be a distinct eigernvalue?
Yes, so far so good. However:Juggler123 said:Well basically the proof (as far as I can see) is asking to show that [itex]\sum _{i=1}^{k}\alpha_iv_i =0[/itex] implies that [itex]\alpha_i=0[/itex] for all i, alpha_i being an element of the field F.
How so?! No, this is not a correct conclusion. You are basically saying if a+b=0 then a=0 and b=0.[tex]\sum _{i=1}^{k}\alpha_iTv_i =\sum _{i=1}^{k}\alpha_ia_iv_i [/tex]
Now for this sum to equal zero then either aplha(i)=0 (as required) OR a(i)v(i)=0,
That's just a matter of definition. A lot of people exclude 0 from being an eigenvector. They then say "the set of all eigenvectors, together with the zero vector, is a subspace". Other authors, like Axler, do the same as you. It doesn't matter, as long as you be clear on your definition.HallsofIvy said:phyzmatic, 0 can be an eigenvector. A number, [itex]\lambda[/itex] is defined to be an eigenvalue of operator T if and only if there exist a non-zero vector v such that [itex]Tv= \lambda v[/itex] but once we have that any vector, including 0, such that [itex]Tv= \lambda v[/itex] is an eigenvector of T.
We need to have 0 an eigenvector in order to say "the set of all eigenvectors of T, corresponding to eigenvalue [itex]\lambda[/itex], is a subspace".
Eigenvalues are a type of scalar number that are associated with a linear transformation or matrix. They represent the scale factor by which a vector is stretched or compressed by the transformation. Eigenvalues are important in science because they provide information about the behavior and characteristics of a system or process.
Yes, zero can be an eigenvalue. A zero eigenvalue indicates that the transformation does not change the direction of the associated eigenvector, but only scales it by a factor of zero. In other words, the eigenvector remains unchanged.
Having distinct eigenvalues means that each eigenvalue is unique and has its own associated eigenvector. This allows for a clear understanding of how the transformation affects different directions in space and can be useful in determining the stability and behavior of a system.
A matrix can only be diagonalizable if it has a complete set of linearly independent eigenvectors. Having distinct eigenvalues ensures that the set of eigenvectors associated with each eigenvalue is linearly independent, making the matrix diagonalizable.
Yes, it is possible for two matrices to have the same distinct eigenvalues. This is because the eigenvalues of a matrix are determined by its characteristic polynomial, which can have the same roots for different matrices. However, the corresponding eigenvectors may be different for each matrix.