Simple formula for Moore-Penrose pseudoinverse

Engineers and physicists find use for the so-called
"Moore-Penrose pseudoinverse" of an operator F: H --> K
between Hilbert spaces H and K (which can be real or complex).
Typically, H and K are finite dimensional
(which will be assumed below for simplicity),
and F is viewed as a matrix.

A common notation for the Moore-Penrose pseudoinverse
(henceforth simply called "pseudoinverse" is F with a superscript + , F^+,
but for ASCII simplicity I will call the pseudoinverse G : K --> H.

Let Null(F) denote the nullspace of F and In(F) its initial space
(defined as the orthogonal complement of Null(F)). As usual, Range(F)
will denote its range. Note for future reference that In(F) = In(F*F),
where F* denotes the adjoint of F.

Most mathematicians would probably define the pseudoinverse G of F
as the unique operator K --> H such that:

(i) G is a left inverse for the restriction of F to its initial
space, i.e.,

GFx = x for all x in In(F),

which defines G on Range(F), and

(ii) G annihilates the orthogonal complement of Range(F).

Definitions in the engineering literature are typically expressed in terms
of matrices and are much more complicated, e.g.,
http://en.wikipedia.org/wiki/Moore-Penrose_pseudoinverse.

It is almost immediate that the pseudoinverse G is given by
the simple formula

(*) G = (F*F)^(-1) F*,

where (F*F)^(-1) denotes the inverse of the restriction of F*F to
In(F*F) = In(F). To see this, just multiply on the right by F to get (i),
and check (ii) separately.

Oddly, formulas for G in the engineering literatures are typically much
more
complicated and generally require diagonalizing F*F, which can be difficult
to do explicitly for F*F large (because its characteristic polynomial
will typically have no algebraically expressible roots).
However, F*F^(-1) can be obtained without diagonalizing F*F. (Just
choose a basis for In(F), represent F*F|In(F) as a matrix with respect
to this basis, and invert.)

I was recently surprised to discover the simple formula (*) when
I had occasion to actually calculate a pseudo-inverse. It is inconceivable
to me that it could be previously unknown, and I would be grateful
for a reference, which I shall need for a physics paper.

For an audience of mathematicians, one could simply state (*)
with minimal comment, but for people who may be more comfortable thinking
in terms of of matrices, (*) may not seem obvious, and a reference is
probably
necessary. Of course, I could devote a few paragraphs to proving (*),
but the paper will have a space limitation which I'd rather use for
the subjects of the paper.

A reference accessible online would be the most helpful. I have
retired to a rural area of Nevada which is 200 miles from the nearest
research library, so to access a book I generally have to buy it.

I thank in advance anyone kind enough to be of help.

Stephen Parrott

 

Your formula is indeed well known, you might look
into books on numerical analysis dealing with least squares,
I recommend the book of Ake Bjoerck
''numerical solution of least squares problems'' (SIAM)
or Lawson-Hanson: ''solving least squares problems'' (Prentice Hall)
It has however severe disadvantages: firstly you assume that F has full column
rank (otherwise the inverse does not exist), secondly, this approach
known in connection with the ''normal equations solution'' of linear
least squares problems, is subject to an unnatural (i.e. avoidable) bad
rounding error amplification (known as ''bad conditioning'').
and moreover, F* F might loose ''sparsity'' even if F is very sparse.
Hence the proper way of using the Moore-Penrose pseudoinverse in
numerical work is using the singular value decomposition, which completely
avoids building F* F . In the large scale case this can be computed also
approximately using only the dominant subspaces.
hth
peter