[SciPy-Dev] SciPy-Dev Digest, Vol 161, Issue 11

Sat Mar 11 10:08:17 EST 2017

I used ipopt for interior-point method from python, are you trying to add
something similar to scipy? if yes, why not just add a wrapper for ipopt,
since the license looks not restrictive?

On Fri, Mar 10, 2017 at 1:40 PM, <scipy-dev-request at scipy.org> wrote:

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> Today's Topics:
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>    1. Re: GSoC2017: Constrained Optimisation in Scipy (Matt Haberland)
>    2. Re: GSoC2017: Constrained Optimisation in Scipy (Nikolay Mayorov)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 10 Mar 2017 10:01:32 -0800
> From: Matt Haberland <haberland at ucla.edu>
> To: SciPy Developers List <scipy-dev at scipy.org>
> Subject: Re: [SciPy-Dev] GSoC2017: Constrained Optimisation in Scipy
> Message-ID:
>         <CADuxUiyK_d3BvjinikmG_k8LTh8JczMFPwboNaSerdnsWFp=yQ@
> mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> The choice of nonlinear optimization algorithm can have a dramatic impact
> on the speed and quality of the solution, and the best choice for a
> particular problem can be difficult to determine a priori, so it is
> important to have multiple options available.
>
> My work in optimal control leads to problems with (almost entirely)
> nonlinear constraints, and the use of derivative information is essential
> for reasonable performance, leaving SLSQP as the only option in SciPy right
> now. However, the problems are also huge and very sparse with a specific
> structure, so SLSQP is not very effective, and not nearly as effective as a
> nonlinear optimization routine could be. So despite SciPy boasting 14
> options for minimization of a nonlinear objective, it wasn't suitable for
> this work (without the use of an external solver).
>
> I think SciPy is in need of at least one solver designed to handle large,
> fully nonlinear problems, and having two would be much better. Interior
> point and SQP are good, complementary options.
>
> On Thu, Mar 9, 2017 at 2:38 PM, Antonio Ribeiro <antonior92 at gmail.com>
> wrote:
>
> > Hello, my name is Antonio and I am a Brazilian electrical engineer
> > currently pursuing my master degree. I have contributed to scipy.optimize
> >  and scipy.signal implementing functions "iirnotch", "irrpeak"
> > <https://github.com/scipy/scipy/pull/6404>and the method
> > "trust-region-exact" <https://github.com/scipy/scipy/pull/6919> (under
> > revision). I am interested in applying for the Google Summer of Code 2017
> > to work with the Scipy optimisation package.
> >
> > My proposal is to improve scipy.optimize adding optimisation methods that
> > are able to deal with non-linear constraints. Currently the only
> > implemented methods able to deal with non-linear constraints are the
> > FORTRAN wrappers SLSQP and COBYLA.
> >
> > SLSQP is a sequential quadratic programming method and COBYLA is a
> > derivative-free optimisation method, they both have its limitations:
> > SLSQP is not able to deal with sparse
> > hessians and jacobians and is unfit for large-scale problems and COBYLA,
> > as other derivative-free methods, is a good choice for optimise noisy
> > objective functions, however usually presents a poorer performance then
> > derivative-based methods when the derivatives are available (or even when
> > they are computed by automatic differentiation or finite differences).
> >
> > My proposal is to implement in Scipy one or more state-of-the-art solvers
> > (interior point and SQP methods) for constrained optimisation problems. I
> > would like to get some feedback about this, discuss the relevance of it
> for
> > Scipy  and get some suggestions of possible mentors.
> >
> > _______________________________________________
> > SciPy-Dev mailing list
> > SciPy-Dev at scipy.org
> > https://mail.scipy.org/mailman/listinfo/scipy-dev
> >
> >
>
>
> --
> Matt Haberland
> Assistant Adjunct Professor in the Program in Computing
> Department of Mathematics
> 7620E Math Sciences Building, UCLA
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> ------------------------------
>
> Message: 2
> Date: Sat, 11 Mar 2017 00:40:00 +0500
> From: Nikolay Mayorov <nikolay.mayorov at zoho.com>
> To: "SciPy Developers List" <scipy-dev at scipy.org>
> Subject: Re: [SciPy-Dev] GSoC2017: Constrained Optimisation in Scipy
> Message-ID: <15ab9bc2975.f4cd34ac5338.5894437124599888508 at zoho.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi, Antonio!
>
>
>
> I too think that moving towards more modern algorithms and their
> implementations is good for scipy. I would be happy to mentor this project,
> and most likely I will be able to.
>
>
>
> Some current thoughts and questions:
>
>
>
> 1. Have you figured what is done in SLSQP (especially "least squares"
> part)? Do you plan to use a similar approach or another approach to SQP? (I
> figured there are several somewhat different approaches.) Setting on a
> literature reference (or most likely several of them) is essential.
>
>
>
> 2. I think it is not wrong to focus on a single solver if you feel that it
> will likely take the whole time. Or maybe you can prioritize: do this first
> for sure and then have alternatives, plan a) to switch to another solver or
> plan b) to improve\add something more minor.
>
>
>
> 3. Consider whether to fit a new solver into minimize or make it as a new
> separate solver. The latter approach gives a freedom to implement things
> exactly as you want (and not to depend on old suboptimal choices) , but I
> guess it can be considered as impractical/inconsistent by some people.
> Maybe it can be decided along the way.
>
>
>
> 4. I think it is important to start to think about benchmark problems
> early, maybe even start with them. It's hard to develop a complicated
> optimization algorithm without ability to see how efficiently it works
> right away.
>
>
>
>
>
>
>
> ---- On Fri, 10 Mar 2017 23:01:32 +0500 Matt Haberland &
> lt;haberland at ucla.edu> wrote ----
>
>
>
>
> The choice of nonlinear optimization algorithm can have a dramatic impact
> on the speed and quality of the solution, and the best choice for a
> particular problem can be difficult to determine a priori, so it is
> important to have multiple options available.
>
>
>
> My work in optimal control leads to problems with (almost entirely)
> nonlinear constraints, and the use of derivative information is essential
> for reasonable performance, leaving SLSQP as the only option in SciPy right
> now. However, the problems are also huge and very sparse with a specific
> structure, so SLSQP is not very effective, and not nearly as effective as a
> nonlinear optimization routine could be. So despite SciPy boasting 14
> options for minimization of a nonlinear objective, it wasn't suitable for
> this work (without the use of an external solver).
>
>
>
> I think SciPy is in need of at least one solver designed to handle large,
> fully nonlinear problems, and having two would be much better. Interior
> point and SQP are good, complementary options.
>
>
>
>
> On Thu, Mar 9, 2017 at 2:38 PM, Antonio Ribeiro <antonior92 at gmail.com>
> wrote:
>
>
>
>
>
>
>
>
>
>
> --
>
> Matt Haberland
>
> Assistant Adjunct Professor in the Program in Computing
>
> Department of Mathematics
>
> 7620E Math Sciences Building, UCLA
>
>
>
>
> _______________________________________________
>
> SciPy-Dev mailing list
>
> SciPy-Dev at scipy.org
>
> https://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
> Hello, my name is Antonio and I am a Brazilian electrical engineer
> currently pursuing my master degree. I have contributed to scipy.optimize
> and scipy.signal implementing functions "iirnotch", "irrpeak"and the method
> "trust-region-exact" (under revision). I am interested in applying for the
> Google Summer of Code 2017 to work with the Scipy optimisation package.
>
> My proposal is to improve scipy.optimize adding optimisation methods that
> are able to deal with non-linear constraints. Currently the only
> implemented methods able to deal with non-linear constraints are the
> FORTRAN wrappers SLSQP and COBYLA.
>
> SLSQP is a sequential quadratic programming method and COBYLA is a
> derivative-free optimisation method, they both have its limitations: SLSQP
> is not able to deal with sparse
> hessians and jacobians and is unfit for large-scale problems and COBYLA,
> as other derivative-free methods, is a good choice for optimise noisy
> objective functions, however usually presents a poorer performance then
> derivative-based methods when the derivatives are available (or even when
> they are computed by automatic differentiation or finite differences).
> My proposal is to implement in Scipy one or more state-of-the-art solvers
> (interior point and SQP methods) for constrained optimisation problems. I
> would like to get some feedback about this, discuss the relevance of it for
> Scipy  and get some suggestions of possible mentors.
>
>
>
>
> _______________________________________________
>
> SciPy-Dev mailing list
>
> SciPy-Dev at scipy.org
>
> https://mail.scipy.org/mailman/listinfo/scipy-dev
>
>
>
>
>
>
>
>
>
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