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Optimisation solvers are complex and incredibly powerful pieces of software. However, there are lots of different options and choosing which to use can be a daunting task.

There are some open source options ([COIN](https://www.coin-or.org/), [OR-tools](https://developers.google.com/optimization), [Minion](https://constraintmodelling.org/minion/), [CVXPY](https://www.cvxpy.org/)) and other commercial offerings ([gurobi](https://www.gurobi.com/), [Mosek](https://www.mosek.com/)), other people write their own for thiner specifics purposes. Some [benchmarks](http://plato.asu.edu/bench.html.) are maintained by Hans Mittelmannt.

Personally, I have used OR-tools, maintained by a team at Google, for vehicle routing optimisation and found it powerful but poorly documented with an inconsistent API. I've also used R's [optim](https://www.rdocumentation.org/packages/stats/versions/3.6.2...) function and [lpsolve](https://cran.r-project.org/web/packages/lpSolve/index.html) for linear and integer problems.


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