python-mip VS SciPy

Compare python-mip vs SciPy and see what are their differences.

python-mip

Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs (by coin-or)
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python-mip SciPy
1 50
496 12,332
2.4% 1.5%
7.1 9.9
17 days ago 4 days ago
Python Python
Eclipse Public License 2.0 BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

python-mip

Posts with mentions or reviews of python-mip. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-20.
  • Ask HN: Do you use an optimization solver? Which one? Why? Do you like it?
    12 projects | news.ycombinator.com | 20 Apr 2022
    I've been using CBC via python-mip (https://github.com/coin-or/python-mip). It's great because it's got a super clean interface (milp variables/expressions/constraints), the code is quite accessible, and it's low overhead which makes it good for solving many very small problems.

    Community sentiment seems to be beginning to shift toward favouring the HiGHS solver (https://github.com/ERGO-Code/HiGHS) over CBC. Something I'm keeping a close eye on.

    nextmv seems to pitch itself as a generic solving ("decision automation") platform or something (unclear). But it seems that the only fleshed out product offering is for vehicle routing, based on the docs. Are there plans to offer, for instance, a solver binary that can be used to solve generic problems?

    Also all the github repos under https://github.com/nextmv-io are private, so links from docs are 404.

SciPy

Posts with mentions or reviews of SciPy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-04.
  • What Is a Schur Decomposition?
    2 projects | news.ycombinator.com | 4 Mar 2024
    I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.

    [1]: https://github.com/scipy/scipy/issues/18566

  • Fortran codes are causing problems
    2 projects | /r/rstats | 13 Sep 2023
    Fortran codes have caused many problems for the Python package Scipy, and some of them are now being rewritten in C: e.g., https://github.com/scipy/scipy/pull/19121. Not only does R have many Fortran codes, there are also many R packages using Fortran codes: https://github.com/r-devel/r-svn, https://github.com/cran?q=&type=&language=fortran&sort=. Modern Fortran is a fine language but most legacy Fortran codes use the F77 style. When I update the R package quantreg, which uses many Fortran codes, I get a lot of warning messages. Not sure how the Fortran codes in the R ecosystem will be dealt with in the future, but they recently caused an issue in R due to the lack of compiler support for Fortran: https://blog.r-project.org/2023/08/23/will-r-work-on-64-bit-arm-windows/index.html. Some renowned packages like glmnet already have their Fortran codes rewritten in C/C++: https://cran.r-project.org/web/packages/glmnet/news/news.html
  • [D] Which BLAS library to choose for apple silicon?
    2 projects | /r/MachineLearning | 24 May 2023
    There are several lessons here: a) vanilla conda-forge numpy and scipy versions come with openblas, and it works pretty well, b) do not use netlib unless your matrices are small and you need to do a lot of SVDs, or idek why c) Apple's veclib/accelerate is super fast, but it is also numerically unstable. So much so that the scipy's devs dropped any support of it back in 2018. Like dang. That said, they are apparently are bring it back in, since the 13.3 release of macOS Ventura saw some major improvements in accelerate performance.
  • SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code
    8 projects | news.ycombinator.com | 18 May 2023
    First, if you read through that scipy issue (https://github.com/scipy/scipy/issues/18118 ) the author was willing and able to relicense PRIMA under a 3-clause BSD license which is perfectly acceptable for scipy.

    For the numerical recipes reference, there is a mention that scipy uses a slightly improved version of Powell's algorithm that is originally due to Forman Acton and presumably published in his popular book on numerical analysis, and that also happens to be described & included in numerical recipes. That is, unless the code scipy uses is copied from numerical recipes, which I presume it isn't, NR having the same algorithm doesn't mean that every other independent implementation of that algorithm falls under NR copyright.

    8 projects | news.ycombinator.com | 18 May 2023
    I guess one of the major issues is the compilation and packaging of the Fortran code. It is also my experience when developing PDFO ( https://www.pdfo.net/ ), the predecessor of PRIMA.

    This is also reflected by the following comment at https://github.com/scipy/scipy/issues/18118#issuecomment-155...

    > The real maintenance burden is the compilation and the packaging not the code itself that we are carrying around.

    8 projects | news.ycombinator.com | 18 May 2023
    see also

    [Optimization Without Derivatives: PRIMA Fortran Version and Inclusion in SciPy](https://news.ycombinator.com/item?id=35959991)

    [SciPy enhancement: The Fortran 77 implementation of COBYLA is buggy and challenging to maintain. Switch to the PRIMA implementation? #18118](https://github.com/scipy/scipy/issues/18118#issuecomment-155...)

  • Optimization Without Using Derivatives
    2 projects | news.ycombinator.com | 21 Apr 2023
    Reading the discussions under a previous thread titled "More Descent, Less Gradient"( https://news.ycombinator.com/item?id=23004026 ), I guess people might be interested in PRIMA ( www.libprima.net ), which provides the reference implementation for Powell's renowned gradient/derivative-free (zeroth-order) optimization methods, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA.

    PRIMA solves general nonlinear optimizaton problems without using derivatives. It implements Powell's solvers in modern Fortran, compling with the Fortran 2008 standard. The implementation is faithful, in the sense of being mathmatically equivalent to Powell's Fortran 77 implementation, but with a better numerical performance. In contrast to the 7939 lines of Fortran 77 code with 244 GOTOs, the new implementation is structured and modularized.

    There is a discussion to include the PRIMA solvers into SciPy ( https://github.com/scipy/scipy/issues/18118 ), replacing the buggy and unmaintained Fortran 77 version of COBYLA, and making the other four solvers available to all SciPy users.

  • What can I contribute to SciPy (or other) with my pure math skill? Iā€™m pen and paper mathematician
    5 projects | /r/Python | 17 Apr 2023
  • Emerging Technologies: Rust in HPC
    3 projects | /r/rust | 24 Mar 2023
    if that makes your eyes bleed, what do you think about this? https://github.com/scipy/scipy/blob/main/scipy/special/specfun/specfun.f (heh)
  • Python
    3 projects | /r/ProgrammerHumor | 29 Dec 2022

What are some alternatives?

When comparing python-mip and SciPy you can also consider the following projects:

SymPy - A computer algebra system written in pure Python

statsmodels - Statsmodels: statistical modeling and econometrics in Python

NumPy - The fundamental package for scientific computing with Python.

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

astropy - Astronomy and astrophysics core library

or-tools - Google's Operations Research tools:

PyMC - Bayesian Modeling and Probabilistic Programming in Python

NetworkX - Network Analysis in Python

Numba - NumPy aware dynamic Python compiler using LLVM

orange - šŸŠ :bar_chart: :bulb: Orange: Interactive data analysis

RDKit - The official sources for the RDKit library

bccb - Incubator for useful bioinformatics code, primarily in Python and R