which, when acted on by a particular linear transformation
, produces a scalar multiple of the original vector. The scalar in question is called the eigenvalue
corresponding to this eigenvector.
It should be noted that "vector" here means "element of a vector space" which can include many mathematical entities. Ordinary vectors are elements of a vector space, and multiplication by a matrix is a linear transformation
on them; smooth functions "are vectors", and many partial differential operators are linear transformations on the space of such functions; quantum-mechanical states "are vectors", and observables
are linear transformations on the state space.
An important theorem says, roughly, that certain linear transformations have enough eigenvectors that they form a basis
of the whole vector states. This is why Fourier analysis
works, and why in quantum mechanics every state is a superposition of eigenstates of observables.
An eigenvector is a (representative member of a) fixed point
of the map on the projective plane
induced by a linear map