is there something similar to Excel Solver in Matlab?

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Andrew
Andrew 2011-8-10
评论： John C L Mayson II 2018-5-2
Hi, I have a similar problem. To simplify assume a linear equation y=mx+c. I have values for the independent variable x, the actual y and want to solve for coefficients m and c. Using Excel Solver I would use random initial values for m and c in my equation, and get a fitted y, say y_fit. Then work the sum of squared residuals between y and y_fit [RSS = sum((y-yfit)^2)]. Then tell Solver to give me a solution for m and c which minimises RSS. Is this possible in Matlab? Can I make use of the Optimisation tool box to do this?

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Titus Edelhofer
Titus Edelhofer 2011-8-10
Hi,
take a look at lsqcurvefit http://www.mathworks.com/help/toolbox/optim/ug/lsqcurvefit.html from Optimization Toolbox. It should do what you are looking for ...
Titus
  2 个评论
Andrew
Andrew 2011-8-12
Thanks Titus. That's what I was looking for. It worked:)
Andrew
John C L Mayson II
John C L Mayson II 2018-5-2
Hi Andrew, I am a beginner in MATLAB and currently struggling with the same problem. I visited the link uploaded by Titus but still couldn't figure it out. I have a set of equations which obtains a variable "b". In one of those equations, I assumed a constant value for a variable "a". Now I want to set variable "b" to 0.01 by changing variable "a". Can you please help if you have the time?

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更多回答（1 个）

Fangjun Jiang
Fangjun Jiang 2011-8-10
From help robustfit.
x = (1:10)';
y = 10 - 2*x + randn(10,1); y(10) = 0;
bls = regress(y,[ones(10,1) x])
brob = robustfit(x,y)
scatter(x,y)
hold on
plot(x,brob(1)+brob(2)*x,'r-', x,bls(1)+bls(2)*x,'m:')
  2 个评论
Andrew
Andrew 2011-8-10
Thanks Fangjun. Problem is that I want to apply solution to a wider problem that may not necessarily be minimising sum of squared residuals of a linear equation. So in this case, is it possible to minimise RSS by changing m and c by means of some iterative process?
Thanks once again.
Fangjun Jiang
Fangjun Jiang 2011-8-10
I am not following. If you assume y=m*x+c, it means linear and the result from regress() is the result of minimizing RSS. If you want to do for example, y=n*x^2+m*x+c, then you can use regress(y,[ones(10,1) x x.^2]). There is no need to do iteration.

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