Interpolation Polynomials and Linear Algebra

We reconsider the theory of Lagrange interpolation polynomials with multiple interpolation points and apply it to linear algebra. For instance, $A$ be a linear operator satisfying a degree $n$ polynomial equation $P(A)=0$. One can see that the evaluation of a meromorphic function $F$ at $A$ is equal to $Q(A)$, where $Q$ is the degree $<n$ interpolation polynomial of $F$ with the the set of interpolation points equal to the set of roots of the polynomial $P$. In particular, for $A$ an $n \times n$ matrix, there is a common belief that for computing $F(A)$ one has to reduce $A$ to its Jordan form. Let $P$ be the characteristic polynomial of $A$. Then by the Cayley-Hamilton theorem, $P(A)=0$. And thus the matrix $F(A)$ can be found without reducing $A$ to its Jordan form. Computation of the Jordan form for $A$ involves many extra computations. In the paper we show that it is not needed. One application is to compute the matrix exponential for a matrix with repeated eigenvalues, thereby solving arbitrary order linear differential equations with constant coefficients.

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