How to define constraints in least square fitting?

Hi,

For a data set (x, y), I am trying to fit a function f(p,x) using lsqcurvefit. I write as follow:

y=ydata;
x=xdata;
 p0=[0.45;0.13];
 %plot(curve2)

lb = [0.1;  0.11 ];
ub = [1.0; 0.16 ];
 options = optimoptions('lsqcurvefit','Display','iter','Algorithm','trust-region-reflective','OptimalityTolerance', 1e-6,'FunctionTolerance',1e-6);
[p,resnorm,residual,exitflag,output,lambda] = lsqcurvefit(@DOS_BCSfit(p,x),p0,x,y,lb,ub,options)

In my case, the final output for my parameters is x=0.1 and 0.16, which is some how just the values defiend in bound constraints.

Can someone help me in pointing out the mistake?

Thanks,

Best regards,

Ritika

[dpb]

There may be no mistake...we can't see what the functional you're trying to fit looks like--quite possible the constraints are such as given that the best fit is at that bound...

[ri_p]

Hi,

Thank you for looking at my question. I attach the data and function that I am trying to fit.

The function looks correct when I plot the function and data together at expected intial values. But, the fitting is not working as expected.

I will be grateful if you can help me in understanding this.

Cheers,

Ritika