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Re: Constrained minimization

From: Juan Pablo Carbajal
Subject: Re: Constrained minimization
Date: Thu, 2 Jan 2020 13:03:52 +0100

> 1. Does this output vector have to be the same length as the input vector?
> If so, what if there are more constraints than components?  Or fewer?
> 2. Is there supposed to be any correlation between the components of the
> output vector and the input vector?  In other words, does output G(1) have
> to depend on X(1) in some manner?  Or does the routine make its own
> determination of how the output depends on the input?
> 3. What happens if the objective function gets driven into an infeasible
> region, such that it produces N/A, NAN, or Inf as an output?  Will the routine
> crash?  If so, can I deal with this in a wrapper function?  If so, what output
> should be provided -- just a very high number?
most of your questions can be answered form the manual entry
Check the example.
If you objective returns nan, the result will be nan, but if the
singularity is isolated sqp might survive.
I have encounter more problems with sqp when the constraints have
singular jacobians even if the singularity is isolated.

> 4. Is there any documentation of the algorithm sqp uses?
It is just iterated QP, see lines 415-421

> 5. Are any of the other options preferable to sqp?
when I am serious about optimization I use nlopt (the octave interface is
outdated, but last time I tried (5.0.0) was still working) and
bayesopt (uses nlopt)
If you update nlopt interface with Octave do let the authors know,
they are (at least were!) very open to collaborations.


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