How To Solve Least Squares Problem. You'll end up with a 0 equals 1. We have a model that will predict y i given x i for some parameters β , f ( x) = x β.
Otherwise, it has infinitely many solutions. For a general matrix, we try to change to the orthogonal case. You can include the factor of 1/2 (harmless) and square the norm, which doesn't affect the solution but needlessly makes the problem solution less numerically robust.
