Linear Optimization with Glop | Hacker News Reader

Linear Optimization with Glop

developers.google.com

47 pointsmzl9 comments

Super cool demo.

Linear programming in C++ with a Python wrapper has been implemented by many other packages too. Does anyone know how Glop compares with scipy.optimize, PuLP, pl_solve, COIN-OR's many projects, or CPLEX and its many Python wrappers?

Generally the wrapper isn't the bit you're worried about, rather the speed and abilities of the underlying solver implementation is the factor you care about most. We use PuLP for our projects as it is compatible with most LP/MIP solvers giving us the ability to code the formulation once then run on a variety of solvers.

I would expect Glop support will be added to PuLP given some time.

They've put together a blog post with a bunch of features, including the fact you can solve LPs in google doc spreadsheets http://googledevelopers.blogspot.com.au/2014/09/sudoku-linea...

Any idea why Google made their own library instead of working on (or forking) another library like GLPK or CLP? And is there any benchmarking data? I couldn't see any on the site.

If I had to guess I'd say this is meant for Google Docs to fill in one of the Excel feature gaps. It probably focuses more on stability/accuracy than speed, and probably won't provide performance comparable to existing packages. Might have grown out of someone's hobby project or a university assignment.

I'm not an expert on LP-packages, but in my small experience most LP packages are not that great in areas such as API design, modularity, and other similar non-mathematical areas that are important to programmers.

My guess is that they wanted something small, simple, and easy to build into other products. I've heard from some of the or_tools developers that the internal usages are most often for rather simple problems. In such cases, ease of use is quite important.

That being said, or_tools is a very competent optimization package. For example see the results in the latest MiniZinc Challenge: http://www.minizinc.org/challenge2014/results2014.html.

Personally, I'm hoping that this is a first step towards Google implementing their own MIP solver.

I think that given the general quality of or_tools, that might be a very practical choice when the full power of something like CPLEX or Gurobi is not needed.

I really want to know what other fellow HNers are using LP for. If you may tell real cases that will be awesome. Mostly I am using it for product mixing and licensing cost optimization..

I'm using LP in production as a solver for LAD(http://en.wikipedia.org/wiki/Least_absolute_deviations). We've thousands of jobs running on Mesos ( see https://news.ycombinator.com/item?id=8394718 ) and I forecast & provision their CPU, Memory consumption over time. So essentially, fit an LAD through thousands of historical time series & predict what happens to the usage in the future, so we can optimally provision clusters. My LP code is essentially this http://bit.ly/1vxknUi , with performance optimizations. So it uses the OSS Apache math libs to do LP, not Glop. But I'd imagine we'd get the same results with Glop, maybe much faster. Google GSON for example is a very fast json parser for huge jsons, which we also use in the same project :)

Used for a similar project where I have to mix providing server resources and limiting the bandwidth. (game servers at EC2)

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