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personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:15] antonello [Installation] |
personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:39] (current) antonello [Further help] |
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You have plenty of development environment to choose from (e.g. Jupiter, Juno), a clear modern language, the possibility to interface your model with third party libraries.. all of this basically for free.\\ | You have plenty of development environment to choose from (e.g. Jupiter, Juno), a clear modern language, the possibility to interface your model with third party libraries.. all of this basically for free.\\ | ||
It is also, at least for my user case, much faster than GAMS. Aside the preparation of the model to pass to the solver, where it is roughly equivalent, in the solver execution I can benefit of having on my system a version of IPOPT compiled with the much more performing ma27 linear solver, while for GAMS I would have to rely on the embedded version that is compiled with the MUMPS linear solver. That's part of the flexibility you gain in using JuMP in place of GAMS. | It is also, at least for my user case, much faster than GAMS. Aside the preparation of the model to pass to the solver, where it is roughly equivalent, in the solver execution I can benefit of having on my system a version of IPOPT compiled with the much more performing ma27 linear solver, while for GAMS I would have to rely on the embedded version that is compiled with the MUMPS linear solver. That's part of the flexibility you gain in using JuMP in place of GAMS. | ||
- | That's said, for people that don't need such flexibility, | + | That's said, for people that don't need such flexibility, |
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==== Importing the libraries ==== | ==== Importing the libraries ==== | ||
- | You will need to import as a minima the '' | + | You will need to import as a minima the '' |
< | < | ||
- | # Import of the JuMP and DataFrames modules (the latter | + | # Import of the JuMP, GLPK, CSV and DataFrames modules (the latter |
- | using JuMP, DataFrames | + | using CSV, DataFrames, GLPK, JuMP |
</ | </ | ||
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# seattle | # seattle | ||
# san-diego | # san-diego | ||
- | d_table = wsv""" | + | d_table = CSV.read(IOBuffer(""" |
plants | plants | ||
seattle | seattle | ||
san_diego | san_diego | ||
- | """ | + | """ |
d = Dict( (r[: | d = Dict( (r[: | ||
# Here we are converting the table in a " | # Here we are converting the table in a " | ||
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Here we declare a JuML optimisation model and we give it a name. This name will be then passed as first argument to all the subsequent operations, like creation of variables, constraints and objective function.\\ | Here we declare a JuML optimisation model and we give it a name. This name will be then passed as first argument to all the subsequent operations, like creation of variables, constraints and objective function.\\ | ||
- | We can, if we wish, works with several models at the same time.\\ | + | The solver engine to use is given as argument of the '' |
- | If we do not specify a solver, we let JuML use a suitable solver for the type of problem. Aside to specify the solver, we can also pass it solver-level options, e.g.: | + | We could pass solver-specific |
- | '' | + | '' |
<code julia> | <code julia> | ||
# Model declaration (transport model) | # Model declaration (transport model) | ||
- | trmodel = Model() | + | trmodel = Model(GLPK.Optimizer) |
</ | </ | ||
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==== Resolution of the model ==== | ==== Resolution of the model ==== | ||
- | It is at this point that the solver is called and the model is passed to the solver engine for its solution. The return value is the status of the optimisation (": | + | It is at this point that the solver is called and the model is passed to the solver engine for its solution. The return value is the status of the optimisation ('' |
<code julia> | <code julia> | ||
- | status = solve(trmodel) | + | optimize!(trmodel) |
+ | status = termination_status(trmodel) | ||
</ | </ | ||
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<code julia> | <code julia> | ||
- | if status == :Optimal | + | if status == MOI.OPTIMAL |
- | println(" | + | println(" |
- | println(getvalue(x)) | + | println(" |
+ | println(value.(x)) | ||
println(" | println(" | ||
- | [println(" | + | [println(" |
println(" | println(" | ||
- | [println(" | + | [println(" |
+ | |||
else | else | ||
println(" | println(" | ||
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==== Editing and running the script ==== | ==== Editing and running the script ==== | ||
Differently from GAMS you can use whatever editor environment you wish to code a JuMP script. If you don't need debugging features, a simple text editor like Notepad++ (in windows), gedit or kate (in Linux) will suffice. They already have syntax highlight for Julia.\\ | Differently from GAMS you can use whatever editor environment you wish to code a JuMP script. If you don't need debugging features, a simple text editor like Notepad++ (in windows), gedit or kate (in Linux) will suffice. They already have syntax highlight for Julia.\\ | ||
- | If you want advanced features and debugging capabilities you can use a dedicated Julia IDE, like e.g. [[http://junolab.org/|Juno]]. | + | If you want advanced features and debugging capabilities you can use a dedicated Julia IDE, like the [[https://www.julia-vscode.org/|Julia extension for VSCode]]. |
- | If you are using instead the Julia console, you can run the script as '' | + | If you are using instead the Julia terminal, you can run the script as '' |
===== Further help ===== | ===== Further help ===== | ||
- | Documentation of JuMP is available from [[https:// | + | Documentation of JuMP is available from [[https:// |
Happy modelling with JuMP ;-) | Happy modelling with JuMP ;-) |