Can NNLS algorithms solve overdetermined systems with positive constraints?

[Sergei_G]

Hello everyone,

I'd like to solve overdetermined system of linear equations (in fact to fit experimental data)
(like y1=C1*X11+C2*X12+...+Cm1*X1m)
y2=C1*X21+C2*X22+..+Cm*X2m
...
yn=C1*Xn1+C2*Xn2+...Cm*Xnm)
sometimes n>>m sometimes n>~m , yi and xij are known coefficients
and I know ab initio that all unknowns C1...Cm are positive. Are there specific algorithms developed for such problem? I tried to solve it with simplest least square, but I always get something like oscillations with increase of m - Positive C are compensated by negative C and fit becomes perfect but it does not have physical sence.

Thanks,

Sergei.

 

[hotvette]

You might find something useful in the following links:

http://www.mathworks.com/access/hel...s+with+non+negative+constraints&aq=f&oq=&aqi=

http://plato.asu.edu/sub/nonlsq.html#lsqres

 

[Sergei_G]

Thanks!

 

[hotvette]

The site below has links to C and Fortran version of NNLS (non-negative least squares) code.

http://hesperia.gsfc.nasa.gov/~schmahl/nnls/