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Dimitri Lozeve 2020-05-27 10:12:00 +02:00
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@ -180,4 +180,38 @@ problem in a readable form.
* Solvers and computational resources <<solvers>>
When you start reading about modelling and algorithms, I recommend you
try solving a few problems yourself, either by hand for small
instances, or using an existing solver. It will allow you to follow
the examples in books, while also practising your modelling
skills. You will also get an intuition of what is difficult to model
and to solve.
There are many solvers available, both free and commercial, with
various capabilities. I recommend you use the fantastic [[https://github.com/JuliaOpt/JuMP.jl][JuMP]] library
for Julia, which exposes a domain-specific language for modelling,
along with interfaces to nearly all major solver packages. (Even if
you don't know Julia, this is a great and easy way to start!) If you'd
rather use Python, you can use Google's [[https://developers.google.com/optimization/introduction/python][OR-Tools]] or [[https://github.com/coin-or/pulp][PuLP]] for linear
programming.
Regarding solvers, there is a [[http://www.juliaopt.org/JuMP.jl/stable/installation/#Getting-Solvers-1][list of solvers]] on JuMP's documentation,
with their capabilities and their license. Free solvers include [[https://www.gnu.org/software/glpk/][GLPK]]
(linear programming), [[https://github.com/coin-or/Ipopt][Ipopt]] (non-linear programming), and [[https://scip.zib.de/][SCIP]]
(mixed-integer linear programming).
Commercial solvers often have better performance, and some of them
propose a free academic license: [[https://www.mosek.com/][MOSEK]], [[https://www.gurobi.com/][Gurobi]], and [[https://www.ibm.com/analytics/cplex-optimizer][IBM CPLEX]] in
particular all offer free academic licenses and work very well with
JuMP.
Another awesome resource is the [[https://neos-server.org/neos/][NEOS Server]]. It offers free computing
resources for numerical optimization, including all major free and
commercial solvers! You can submit jobs on it in a standard format, or
interface your favourite programming language with it. The fact that
such an amazing resource exists for free, for everyone is
extraordinary. They also have an accompanying book, the [[https://neos-guide.org/][NEOS Guide]],
containing many case studies and description of problem types. The
[[https://neos-guide.org/content/optimization-taxonomy][taxonomy]] may be particularly useful.
* References