pyomo VS stan

Pyomo: We tried pyomo still using the same matrix representation as above (5-minutes timeslot interval), but still encountered the same difficulty of expressing program durations as constraint. I seem to not able to make a condition inside the constraint declaration such that if this matrix entry is 1, then do this operation.

Alternative: pyomo

I’m looking for something similar to Pyomo for Python. Something that connects on the backend to something like GLPK, CBC, IPOPT. Using Google, I’ve only been able to find a few linear programming libraries. If anyone could point me the right direction, it would be greatly appreciated!

Pyomo, it even has its own book. Additionally, CVXOPT focuses on convex optimization, PuLP on linear programming (it has lots of interfaces for other solvers).

There are some optimization modeling tools, Pulp andScipy are known for linear programming (LP) modeling, CVXOPT and Pyomo for quadratic programming (QP).

To rephrase this in math terms, you're looking for the least expensive possible path that covers every node in your yard. As for tools, if you don't mind programming in python, maybe try this: http://www.pyomo.org/.

For modelling libraries in general-purpose languages, Gurobi's python bindings have the best reputation. But of course Gurobi is very expensive (I have heard about $50k for a fully unrestricted license, plus $10k yearly for support). On the open-source side, besides Google's OR-Tools, there is Pyomo [1] and PuLP [2] in Python (as the article mentions). In Julia, there is JuMP [3], whose development community is extremely enthusiastic.

Traditionally, however, mathematical models were encoded in domain-specific languages. The most prominent one is AMPL [4] which is proprietary. The glpk [5] people have developed a very neat open source clone of AMPL: the GNU MathProg language. For a more modern take on AMPL-type modelling DSLs, look at ZIMPL [6], which is open source as well.

[1] http://www.pyomo.org/

[2] https://coin-or.github.io/pulp/

[3] https://jump.dev/JuMP.jl/stable/

[4] https://ampl.com

[5] https://www.gnu.org/software/glpk/

[6] https://zimpl.zib.de/

Alternatives: stan and edward

remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.

Here is a good breakdown of recommendations from Andrew Gelman.

For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.

I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.

oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing

[1] https://mc-stan.org/