[SciPy-user] Linear regression with constraints

Jose Luis Gomez Dans josegomez@gmx....
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?

249 Spiele für nur 1 Preis. Die GMX Spieleflatrate schon ab 9,90 Euro.
Neu: Asterix bei den Olympischen Spielen: http://flat.games.gmx.de

More information about the SciPy-user mailing list