nlsq & nnnlsq Least squares

Robust & non negative non linear least squares: nlsq & nnnlsq
Updated 31 Dec 2017

View License

Robust & non negative non linear least squares: nlsq & nnnlsq
nlsq Robust non linear least squares
uses singular value decomposition and attempts
a solution to singular and near singular cases.
nnnlsq Robust non negative non linear least squares
uses robust version of nnls and constrains parameters
to be positive.
Both can be used with regularisation techniques to solve
ill conditioned problems.
p=nlsq(@fnct,data,p0) % robust non linear least squares
p=nnnlsq(@fnct,data,p0) % non linear non negative least squares
err=fnct(p,data) calculates a vector of error terms for parameters p,
optionally [err,der]=fnct(p,data) for given derivatives.
data contains all the values needed by fnct to calculate
the error terms s. Can be any type but typically a struct.
p0 is an initial value for the parameters p. Note for singular
cases fitted parameters p may vary with p0

For regularisation append in fnct a small multiple of p to the error
terms calculated.

See nlsqdemo.html, doc nlsq, doc nnnlsq

38003 nnls - Non negative least squares
38881 Optional function arguments

Will run ok in earlier Matlab versions.

Cite As

Bill Whiten (2024). nlsq & nnnlsq Least squares (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes

Added image
Signal processing toolbox not required