How can I implement lsqcurvefit function on my equations?

Hello, I have a nonlinear equation system of 5 variables (complex-valued) and I want to try “lsqcurvefit” function. I want my output to be as close to zero as possible so, should "ydata" must be equal to my expected values or a row vector of 5 zeros? Sorry if it's a dumb question but I am confused. Thanks for the help.

objfcn = @(x,xdata) [...Equations...];
  
x0 = Initial_Values(1,:)';
xdata = Measurements(1,:)';
ydata = Exp_XValues(1,:)';
[x,fval] = lsqcurvefit(objfcn, x0, xdata, ydata, [], []);

[Matt J]

You mean you want the x(i) to be as close to zero as possible? You think the optimal parameters are non-unique?

[Ceyhan TURKMEN]

Actually, I search optimum x(i) values, whose roots of the equation set.

Answer 1

[Jon]

From your description, it sound like you want to find the solution to a system of 5 equations (in I assume 5 unknowns). In this case I think fsolve would be more appropriate. lsqcurvefit is for the situation where you are trying to find the coefficients of a nonlinear expression to give the best fit to an observed data set. In particular this would be the situation where you have typically many more equations than unknowns, and your unknowns are the parameters of some nonlinear equation. So for example if you were trying to fit an equation y = a*x^b + c and you wanted to find the best values for a,b and c.

[Ceyhan TURKMEN]

Thank you for your answer. It seems like fsolve is more suitable for this problem as you said. I will applied fsolve to solve the equations.

[Jon]

Glad to hear you are able to move ahead now