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- Newsgroups: sci.math.num-analysis
- Path: sparky!uunet!spool.mu.edu!darwin.sura.net!jvnc.net!nuscc!engp2116
- From: engp2116@nuscc.nus.sg (Rainer Bachl)
- Subject: Re: Probl. Lin.-Algeb./Optim.
- Message-ID: <1992Nov21.102847.17036@nuscc.nus.sg>
- Organization: National University of Singapore
- X-Newsreader: Tin 1.1 PL4
- References: <1992Nov20.035117.9332@nuscc.nus.sg>
- Date: Sat, 21 Nov 1992 10:28:47 GMT
- Lines: 37
-
- engp2116@nuscc.nus.sg (Rainer Bachl) writes:
- :
- : 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.
-
- Sorry, forgot to mention an arbitrary norm constraint for the desired
- solution, e.g. | d | = 1, to avoid the trivial case d = 0.
- 2
- -Rainer Bachl
- National University of Singapore
-