Spectrum of a matrix

In mathematics, the spectrum of a matrix is the set of its eigenvalues. More generally, if $T:V\to V$ is a linear operator over any vector space, its spectrum is the set of scalars $\lambda$ such that $T-\lambda\cdot id$ is not invertible. The determinant of the matrix equals the product of its eigenvalues. Similarly, the trace of the matrix equals the sum of its eigenvalues. From this point of view, we can define the pseudo-determinant for a singular matrix to be the product of its nonzero eigenvalues (the density of multivariate normal distribution will need this quantity).

Definition

Let V be a finite-dimensional vector space over some field K and suppose T: V → V is a linear map. The spectrum of T, denoted σ_T, is the multiset of roots of the characteristic polynomial of T. Thus the elements of the spectrum are precisely the eigenvalues of T, and the multiplicity of an eigenvalue λ in the spectrum equals the dimension of the generalized eigenspace of T for λ (also called the algebraic multiplicity of λ).

Now, fix a basis B of V over K and suppose M∈Mat_K(V) is a matrix. Define the linear map T: V→V point-wise by Tx=Mx, where on the right-hand side x is interpreted as a column vector and M acts on x by matrix multiplication. We now say that x∈V is an eigenvector of M if x is an eigenvector of T. Similarly, λ∈K is an eigenvalue of M if it is an eigenvalue of T, and with the same multiplicity, and the spectrum of M, written σ_M, is the multiset of all such eigenvalues.