Semi-Inversion of a Diagonalizable Nonsingular Matrix



Donald R. Burleson, Ph.D.

Copyright © June 2017 by Donald R. Burleson. All rights reserved.



In my previous article "The Semi-Inversion Operator and the Pauli Particle-Spin Matrices," at www.blackmesapress.com/Semi-inverses.htm , I proposed the concept of the (principal) semi-inverse of a nonsingular matrix A, so designated because by the proposed transform (A) = Ai , a successive application of the transform formally gives the ordinary inverse of A. This semi-inversion operator was described as essentially being handled computationally by the eigenvalue relation extended to exponentiation of the eigenvalues by the power .

In the earlier article I developed a formula for semi-inverting a 2X2 nonsingular matrix with distinct eigenvalues, where the same general approach (more laboriously) can be pursued for larger matrices.

A considerable gain in ease of computation of the semi-inverse is at hand when the square nonsingular matrix, of whatever size, is diagonalizable, whether its eigenvalues are distinct or not (i.e. provided the vector space Fn X 1 has an eigenvector basis, a necessary and sufficient condition for diagonalizability). It seems prudent at this point simply to state and prove the following:



THEOREM: Every diagonalizable nonsingular matrix is semi-invertible.



PROOF: Let A be any diagonalizable and invertible matrix. Then as A is diagonalizable there is a nonsingular modal matrix M such that , where Dg(j) denotes the diagonal matrix having the eigenvalues of A on the diagonal, and where M, as is customary, is formed by taking respective eigenvectors as columns. But then exponentiation of A by semi-inversion merely consists of formally computing Ai as Alternatively, since a matrix A is diagonalizable if and only if it has a spectral decomposition (where the matrices Ej are the principal idempotents of A and the summation is taken over the distinct eigenvalues comprising the spectrum of A), and since for any holomorphic function f the matrix f(A) (with A diagonalizable) can be computed as, it follows that the semi-inverse of a diagonalizable nonsingular matrix A may be computed as , where the exponentiations on the eigenvalues (nonzero since A is invertible) are defined by Euler's formula , i.e. since we have, for , (j) i = cos ln (j) + i sin ln (j) , and likewise if we take which in principal complex value as implied by Euler's relation may be characterized as , . The result of this process is the semi-inverse of A given itself already in diagonalized form, and in this form it is evident that a second exponentiation of the same kind produces eigenvalues that are the reciprocals of the original ones, in keeping with the fact that the inverse of A has reciprocal eigenvalues. That is to say, the semi-inverse of the semi-inverse is the inverse. This completes the proof.



COROLLARY: If a matrix A is diagonalizable and nonsingular and if z is any complex number, then the matrix Az exists.

PROOF: By the theorem, the semi-inverse of A is well-defined, and if z = x+yi one may compute by diagonalization.



EXAMPLE 1: Let A =

with eigenvalues . Since A is 3X3 and has three distinct (and nonzero) eigenvalues, A is diagonalizable (and nonsingular). For the diagonalization of A, the modal matrix M will have as its columns the respective eigenvectors (1,0,0)T, (1,0,-1)T, and (2,3,1)T so that

The canonical diagonalization of A is then

and if denotes the matrix-valued transform that maps A to its semi-inverse, then the semi-inverse of A is found by simply replacing each eigenvalue in the diagonal matrix with its exponentiation, where in principal complex value The result would work out to be

Alternatively, from the diagonalization A = M[Dg(j)]M-1 we could have determined the principal idempotent decomposition (spectral decomposition)



and the semi-inversion is performed by simply exponentiating the eigenvalues

1, -1, and 2 with the same results as before.



EXAMPLE 2: Even if the eigenvalues are not all distinct (so long as there is still an eigenbasis so that the matrix is diagonalizable) the semi-inverse can be computed the same way as in the previous example. E.g. for

with eigenvalues and eigenvectors

(1,2,1)T , (-1,1,0)T , and (-1,0,1)T respectively, we have the modal matrix



with inverse

so that from the resulting diagonalization the desired semi-inverse is



= .



It should be mentioned that in terms of the techniques described here, a diagonalizable matrix A always needs also to be invertible (diagonalizability and invertibility being independent conditions) because otherwise if one of the eigenvalues were the corresponding eigenvalue exponentiations would have to include the undefined matrix element and if such an element were to occur then a further exponentiation would yield which of course is meaningless. But the combined requirements of invertibility and diagonalizability are always sufficient for the semi-inverse of a matrix to exist.



However, consider also the following.

Let with eigenvalues and with inverse

. A is invertible but not diagonalizable because its double eigenvalue generates an eigenspace that is only one-dimensional, i.e. there is no eigenbasis. But we may still semi-invert this matrix.

In general I define a semi-inversion operator as a matrix-valued transform that has, for matrices in its domain, the property . Then consider the motivation provided by the fact that one may easily show by mathematical induction that for n any positive integer (reflecting powers of A) or any negative integer (reflecting powers of its inverse). So for any matrix of the form B = it would be natural to define (B) = with the result

as required. For invertible but non-diagonalizable matrices of the form

one may similarly define the semi-inverse as .



Thus by example together with the results obtained in the previous article cited, we have proved the following:



THEOREM: For nonsingular matrices, diagonalizability is a sufficient but not in general a necessary condition for semi-invertibility.