Change your loss function to a penalized form. IE: Instead of minimizing L(x) s.t. f_1(x) (necessary constraint) f_2(x) (nice but not necessary constraint) .... Do this instead: Minimize L(x) + \lambda * f_2(x) s.t. f_1(x) where \lambda can be a hyper parameter you can tune to trade off how important constraint f_2 is relative to loss function quality. Alternatively: if you are doing minimization on a probability simplex, you can probably re-parametrize your problem so the only viable solution automatically satisfies those probabilities. The most common way is to run your output through a softmax ( https://en.wikipedia.org/wiki/Softmax_function) On Tue, Feb 5, 2019 at 9:06 AM <scipy-user-request@python.org> wrote:

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Today's Topics:

1. A priority of constrains for scipy.optimize.minimize (Jan Hendrik Berlin)

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Message: 1 Date: Tue, 5 Feb 2019 02:07:35 +0100 From: Jan Hendrik Berlin <jan-hendrik.berlin@cs.uni-dortmund.de> To: SciPy-User <scipy-user@python.org> Subject: [SciPy-User] A priority of constrains for scipy.optimize.minimize Message-ID: <3953ef2d-0660-8fe6-7e04-45b8f2c2735a@cs.uni-dortmund.de> Content-Type: text/plain; charset=utf-8; format=flowed

Hi,

I am solving a problem with some constrains. At first there is a constraint, that the sum of the percentages must be 1. The single percentage could be in the range from 0,0 to 1. And this is the main constraint. On the other side there are some constrains belonging to stuff of the calculation. It is possible, that this constrains are to strong and the solver can't get a solution. In this Case I want to have an option to get a solution respecting the first constraint.

Has anybody an idea of a solution? I think there is no option for a priority of the constrains.

kind regards Jan Hendrik Berlin

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