[SciPy-user] Linear regression with constraints
Jose Luis Gomez Dans
Wed May 7 11:01:42 CDT 2008
I have a set of data (x_i,y_i), and would like to carry out a linear regression using least squares. Further, the slope and intercept are bound (they have to be between 0 and slope_max and 0 and slope_min, respectively).
I have though of using one of the "easy to remember" :D optimization methods in scipy that allow boundaries (BFGS, for example). i can write the equation for the slope and intercept based on x_i and y_i, but I gather that I must provide a gradient estimate of the function at the point of evaluation. How does one go about this? Is this a 2-element array of grad(L) at m_eval, c_eval?
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