MATLAB: Initlalize several variables at once

initializeOptimization Toolbox

I currently have a code where I am initializing many parameters, that will be solved for later.

I do this by:

  C1=params(1);
  C2=params(2);
  C3=params(3);
  C4=params(4);
  C5=params(5);
  C6=params(6);
  C7=params(7);
  C8=params(8);
  C9=params(9);
  C10=params(10);
  C11=params(11);
  C12=params(12);
  C13=params(13);

There's got to be an easier way…

Additionally, I set my intial guesses like:

  params0=[0.1,0.1,0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1];

Is there a way to more easily set this, assuming they all have the same initial guess? Typing them all out that way is tedious, and my code is constantly changing. How can I automate it?

Best Answer

If all you're trying to do is polynomial surface fits of various orders, I would just use the Curve Fitting Toolbox, if you have it

f = fit([x,y],z,  'poly55')

Related Solutions

MATLAB: General Approach to Fitting dozens of curves using the same coefficients

You can use lsqcurvefit to do so, simply by posing the problem as a 30-parameter fit. If you do this, you will want to provide a sparse Jacobian calculation of your own.

However, it might be more efficient, since your eta change gradually, to just run a loop over the data sets, each time initializing eta with the final value from the previous fit.

MATLAB: Estimating initial values for nlinfit or lsqcurvefit

If you expand out your expression, you'll see that you have a polynomial up to x^3, so there are four coefficients including the intercept. But you have specified five coefficients. Try this instead:

modelFun = @(p,x) p(1)*(x-p(4)) + p(2)*(x-p(4)).^2 + p(3)*(x-p(4)).^3;
p0 = [1 1 1 500];

Then use p0 as your initial values. You may find that both nlinfit and lsqcurvefit will have an easier time of it if you avoid trying to include the fifth (overdetermined) parameter. Then, if you like that, you can notice that you get the same results this way:

poly = polyfit(x,y,3);
line(x,polyval(poly,x),'color','c')

Your function is equivalent to a third-order polynomial, and it can be fit by linear least squares with no nonlinear fitting required.

Best Answer

Related Solutions

MATLAB: General Approach to Fitting dozens of curves using the same coefficients

MATLAB: Estimating initial values for nlinfit or lsqcurvefit

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