eigenvalue

Also, it's not just that 1 is an eigenvalue, it is that 1 is the largest eigenvalue.

Is there an easy way to compute the second smallest eigenvalue in question if the graph is large?

The eigenvalue is the amount the eigenvector is scaled up or down when going through the matrix.

An eigenspace is the set of all points in the 2D plane for which the transformation becomes scaling by an eigenvalue.

I'm not sure what an eigenvalue is, though, but I'm sticking to my claim of understanding.

Since we cannot describe a shearing motion in terms of a rotation+scaling+reflection then we no longer get the simple eigenvalue picture above.

Part of it was actually probably my not-quite-conscious perception of eigenvalue's ideas as pretty unimaginative and lacking in potential.

Instead of telling everyone to just use the inferior method of using generic eigenvalue solvers, use better specialised methods.

This satisfies the definition of eigenvector and the corresponding diagonal entry of A is the eigenvalue.

It's not that it's wrong, I just felt eigenvalue was so quick to jump to that, you know?

But none of this has anything to do with what eigenvalue said.

But that's not what eigenvalue was saying, or rather that's not how I read it.

If you compute M^∞, the 1/4 and 1/2 "eigenspaces" will disappear, so you're left with the subspace of the eigenvalue 1.

And the third says, "if we can do X, then v is an eigenvector and \lambda an eigenvalue".

But it's not possible for bad copy-editing to turn 'eigenvalue' into "Igon Value."

If the term "igon value" had been replaced with "eigenvalue", would the usage or context have been incorrect?