Adding the three equations for gives the necessary solvability condition . Conversely, subtraction of the first two and use of the third forceIf , these values also satisfy the first two equations: for example,The analogous calculation gives . Thus existence and uniqueness hold exactly under the stated compatibility condition:
For the three-variable system the matrix and data vector arePerform Gaussian elimination on the augmented matrix. The operations and giveEach operation is an invertible row scaling followed by a row addition, so the solution set is preserved. Next givesBack substitution now yieldsand thenAll three pivots are nonzero. Thus the solution exists uniquely for every , the matrix rank is , and the kernel of a linear map represented by is . The general rank-nullity theorem for a matrix with columns ishere .
Finally, reverse the viewpoint and regard as prescribed. The necessary and sufficient condition already proved applies with , , . Their sum is . Consequently can be recovered exactly whenWhen this holds, their unique values are and . The allowable triples form an affine plane, not all of .
A complex square matrix is a unitary matrix when , equivalently . Here denotes the adjoint matrix, the conjugate transpose. Such a matrix preserves the standard complex inner product and its Euclidean norm.
A real symmetric matrix is a Hermitian matrix, so . For arbitrary complex ,The imaginary cross terms cancel because is Hermitian. Reversing the signs gives the same identity:In particular, implies , so has zero kernel of a linear map. It is a square matrix, and the rank-nullity theorem therefore makes it invertible. The same argument proves invertibility of .
The proposed matrixis consequently well-defined. For any , put . The two norm identities show directly thatTo verify the unitary matrix identity explicitly, take adjoints:The two shifts commute, because both are polynomials in . HenceThusThe imaginary shifts are invertible, and their equal norm changes cancel in the quotient. This is the Cayley transform of a Hermitian matrix in the convention.
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