# Linear loss function for multi-member interpolation

• How can a multimember of one variable at a given Python point using the loss function not as squares but simply distances from points to the polynomial(s)?

• Interesting question. There are some ideas on this account, but first we'll see how the classic version of this task works - the data represent a set of points - a pair of coordinates: Explanation of expression S - sum of squares of deviations (there will be modules, but later) of a set of points from a multi-member (dependent on the set of unknown coefficients) later the values of the ratios at which the sum is minimal are calculated. This is achieved at a point where all private derivatives 0. So...  These expressions form the system n linear equations relative to unknown coefficients They're here, for example with help. numpy.linalg.solve

In our case, S Instead of a square, the module is: So, Where? sgn(x) Formal production module: -1 for x RE 0 and 1 at x grad 0. Now it's a system. n I don't understand any equations ♪ I'm sure these coefficients can be found by some kind of iterative method, but I don't know how yet.

I hope my remarks were useful. Can anyone else tell us how to proceed?

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