[SciPy-User] scipy optimization: inequality constraint type

[Kirill Balunov]

No, no, the constraints only affect the feseable set of the problem. Min or
Max depends on the sign of objective function. From mathemtical point of
view, the problem is that the KKT conditions are derived for standard
formulation (with "less than ..") of NLP.

Cheers,

-gdg


2017-05-26 0:36 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:

> Ah, yes, that convention i am familiar with; maybe to accommodate the
> "inflexibility" of less numerate potential users (who may be fixated, e.g.,
> on wanting to maximize profit or yield)?  Of course, at some point such
> people may want to minimize something, so hopefully they have someone
> around to tell them to simply multiply by negative one. ;-)
>
> DLG
>
> On Thu, May 25, 2017 at 2:29 PM Kirill Balunov <kirillbalunov at gmail.com>
> wrote:
>
>> I'm sorry, perhaps I should more clearly formulate the question. David
>> you are totally right. What I mean by classical: is "less than or equal"
>> type. Of course it's a question of a sign, but still...
>>
>> -gdg
>>
>> 2017-05-26 0:07 GMT+03:00 David Goldsmith <eulergaussriemann at gmail.com>:
>>
>>> I would assume that it's because the "or equal to" option allows greater
>>> flexibility: if that criterion is allowed by the problem, and the algorithm
>>> can find such a solution (e.g., by checking all such corner points), then
>>> that's better than not even providing the option, yes?  And if you try to
>>> "rig" the strict inequality approach by allowing for a little extra room
>>> around the corner, then the exact solution might not be found, yes?
>>> Indeed, i'm no expert, but i did have a course in this, and IIRC, if your
>>> problem allows for equality, then you _must_ separately check all the
>>> corners, yes?  (In other words, what you state about the "classical
>>> formulation" is not what i was taught: I was taught that the specifics of
>>> the problem dictate whether any given inequality should be strict or
>>> "weak.")
>>>
>>> DLG
>>>
>>> On Thu, May 25, 2017 at 1:49 PM Kirill Balunov <kirillbalunov at gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>> I've tried some scipy optimization routines, they work great!!! But I
>>>> wondered, why historically for inequality constraints the type was chosen
>>>> to be "greater than or equal" type? This is inconsistent with the classical
>>>> formulation of non-linear programming problems.
>>>>
>>>> Thanks!
>>>>
>>>> -gdg
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