/mit/matlab/lib/contrib/nlls.m
function [B,chisq,cov]=nlls(x,y,sigma,A,indx,func,plt,Niter,tol,relax)
[B,chisq,cov]=nlls(x,y,sigma,A,[1 2 3 4 5],'fit_absorb')


leastsq(FUN,x,OPTIONS,GRADFUN,P1,P2,...,P10)
/mit/matlab/lib/optim/leastsq.m
leastsq('fitfun2', lam,OPTIONS)

bandemo.m:              disp('[x,options]=leastsq(f,x,OPTIONS,GRAD);')
bandemo.m:              [x,options]=leastsq(f,x,OPTIONS,GRAD);
datdemo.m:              disp('[lam,OPTIONS]=leastsq(''fitfun2'', lam,OPTIONS);')
datdemo.m:              [lam,OPTIONS]=leastsq('fitfun2', lam,OPTIONS);
fsolve.m:evalstr='[x,OPTIONS]=leastsq(FUN,x,OPTIONS,GRADFUN';
leastsq.m:function [x,OPTIONS] = leastsq(FUN,x,OPTIONS,GRADFUN,P1,P2,P3,P4,P5,P6,P7,P8,P9,P10)
lsint.m:% This file initializes the leastsq routine.
toptim.m:       '[x,opts] = leastsq(f,x0,o,[],5);                            '
toptim.m:       '[x,opts] = leastsq(f,x0,o,g,5);                             '
toptim.m:       'o(7)=1; [x,opts] = leastsq(f,x0,o,g,5);                  %15'
toptim.m:       'o(9)=1; [x,opts] = leastsq(f,x0,o,g,5);                     '
tutdemo.m:%   routines: attgoal, minimax, leastsq, fsolve.
%       X=LEASTSQ('FUN',X0) starts at the matrix X0 and finds a minimum to the
%       sum of squares of the functions described in FUN. FUN is usually
%       and  M-file which returns a matrix of objective functions: F=FUN(X).
%
%