[Rd] Improved LP/MIP solver
@vr@h@m@@d|er @end|ng |rom gm@||@com
Sun Dec 12 17:24:42 CET 2021
On Sun, Dec 12, 2021 at 3:44 PM Julian Hall <jajhall using ed.ac.uk> wrote:
> Dear All,
> I am leading the development of HiGHS, which is now the top performing open source linear optimization software on the industry standard benchmarks. In particular, our MIP solver out-performs SCIP, and is way ahead of the COIN-OR solver Cbc.
> HiGHS solves LPs via simplex or interior point, MIPs via branch-and-cut, and QPs via an active set method.
> We were wondering what interest there would be in developing an R interface to HiGHS. I'm not an R user, but have done a bit of searching and see references to Rsymphony and an interface to Lpsolve.
> Performance-wise Lpsolve is very poor, but I know that it has a community of devoted followers. I've not seen benchmark results for Symphony, but I know that Cbc is the preferred COIN-OR MIP solver when it comes to general performance. And, as I observed, the performance of HiGHS is way better than Cbc.
> Are people in the R community tearing their hair out over the performance of software requiring the solution of LPs or MIPs?
> Would a significantly better LP/MIP solver be valuable to the R community?
> Dr. J. A. Julian Hall, Reader, School of Mathematics,
> University of Edinburgh, James Clerk Maxwell Building,
> Peter Guthrie Tait Road, EDINBURGH, EH9 3FD, UK.
> Room: 5418 Phone: [+44](131) 650 5075 Email: J.A.J.Hall using ed.ac.uk<mailto:J.A.J.Hall using ed.ac.uk>
> Web: https://www.maths.ed.ac.uk/school-of-mathematics/people/a-z?person=47
> My working hours may not be your working hours. Do not feel pressure to reply to this email outside your working hours.
> The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.
I cannot speak for the R community, but as someone who needs
optimization on a regular basis, this sounds intriguing. The fact that
HiGHS appears to be FLOSS, and thus usable as-is in the corporate
setting, appeals to those of us who use R in industry. Would you have
any statistics on how the solvers in HiGHS compare with similar ones
currently available in R, specifically the following in NLOPT 
(which is called through nloptr): SLSQP (gradient-based) and COBYLA
(gradient-free) both of which support equality and inequality
constraints, and MMA/CCSA (gradient based) which supports inequality
constraints? As for integer or mixed integer programming, I believe
that there is a lot of room for improvement in R. Personally, I've
resorted to using DEOptim with the "fnMap" entry calling a round
function similar to . So speaking for myself, giving richer options
for optimization is a good thing, especially if the installation
procedure can be simplified!
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