// Check for elements in lb

function y=yth(t, x)
y  = x(1)*exp(-x(2)*t)
endfunction
// we have the m measures (ti, yi):
m = 10;
tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
// measure weights (here all equal to 1...)
wm = ones(m,1);
// and we want to find the parameters x such that the model fits the given
// data in the least square sense:
//
//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
// initial parameters guess
x0 = [1.5; 0.8];
// in the first examples, we define the function fun and dfun
// in scilab language
function y=myfun(x, tm, ym, wm)
y = wm.*( yth(tm, x) - ym )
endfunction

lb = [0 0 0]
ub = [10 10]

//Error
//lsqnonlin: The Lower Bound is not equal to the number of variables
//at line     246 of function lsqnonlin called by :  
//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)

[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)