[Numpy-discussion] How can I constrain linear_least_squares to integer solutions?

Stefan van der Walt stefan at sun.ac.za
Wed Nov 28 02:59:37 EST 2007

On Tue, Nov 27, 2007 at 11:07:30PM -0700, Charles R Harris wrote:
> This is not a trivial problem, as you can see by googling mixed integer least
> squares (MILS). Much will depend on the nature of the parameters, the number of
> variables you are using in the fit, and how exact the solution needs to be. One
> approach would be to start by rounding the coefficients that must be integer
> and improve the solution using annealing or genetic algorithms to jig the
> integer coefficients while fitting the remainder in the usual least square way,
> but that wouldn't have the elegance of some of the specific methods used for
> this sort of problem. However, I don't know of a package in scipy that
> implements those more sophisticated algorithms, perhaps someone else on this
> list who knows more about these things than I can point you in the right
> direction.

Would this be a good candidate for a genetic algorithm?  I haven't
used GA before, so I don't know the typical rate of convergence or its
applicability to optimization problems.


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