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I have found through my reading that applying linear programming optimization techniques are substantially more expensive compared to mean squared error-based methods.

Could someone please help in explaining the computational cost of Linear programming optimization techniques in a simple way.

jeza
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    The two optimize different objective functions, and linear programming optimizes given a set of linear constraints, which most applications of MSE-based methods don't have to deal with, so they aren't really applicable to the same problems. Given that, it seems to me that comparisons aren't likely to be useful... – jbowman Dec 01 '18 at 18:09
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    Hi: note that minimizing mean squared error is not always optimal so it may be more appropriate to use LP if one has a more complex objective function or if there are constraints that are not easily accomodated in the mean squared error minimization. So, it can be the case that LP is useful but, as jbowman pointed out, they are two totally different approaches and the choice of which to use is mostly driven by what the objective function is. – mlofton Dec 01 '18 at 22:46
  • See https://math.stackexchange.com/questions/2610607/is-all-linear-programming-lp-problems-solvable-in-polynomial-time – kjetil b halvorsen Dec 02 '18 at 16:15

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