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Given a set of data {(xi, yi)| i = 1,2,...,m} and the regression equation f(x) = ax + b, I want to use the simplex method to minimize the equation Sigma [(yi - f(xi))/f(xi)]^2. However, I am stuck on how to initially organize the problem. I am not sure whether the equation, Sigma [(yi - f(xi))/f(xi)]^2, needs to be put into some sort of standard form or not. Also, I am having trouble comprehending how to turn the individual [(y - f(x))/f(x)]^2 equations into constraints.

The end goal for this is to turn it into a program. The simplex method should run sufficiently fast for the type of data I will be feeding the program. However, I first need to understand the logic before any code gets written.

Let me know if I'm not stating the problem clear enough. Thank you for helping.

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# Ax + b Least Squares Minimization Standard Form

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