🚀 go-pugleaf

After some efforts and pages of manipulations of an engineering problem
in signal processing I have got a nice mathematical formulation for it:

Find a real vector d constrained to a convex subspace subject to

             min | A * P |
                          2

where P is a real orthogonal projector on the subspace          
of span{ diag(d) * H }, i.e.

                                 T        2  -1    T
             P = diag(d) * H * {H *diag(d) H}   * H  * diag(d)

and A,H are both known real matrices. However, H is m*n with m>n and
satisfies
              T
             H  * H = identity(n).

I think there exists no closed form solution and therefore I am looking
for an optimization procedure locally (if not globally) minimizing the
above cost function. Iterative schemes, similar to that of Steiglitz/
McBride or the IQML algorithm, whereby d(i+1) is calculated by using
a fixed d(i) in the matrix inverse, do not converge to a local minimum
close to the initial value of d. Methods involving first and second
order derivatives might be computationally too expensive for this
application.

I am not an expert in this area and any suggestions are welcome.