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personal:blog:2017:0203_jump_for_gams_users [2023/12/22 10:15] – [Installation] antonellopersonal:blog:2017:0203_jump_for_gams_users [2025/05/02 09:41] (current) – external edit 127.0.0.1
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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, the package automatically install a local pre-compiled version of the solver, so just adding the package relative to the solver is enough to start writing the model. Even more, for people that doesn't care too much about performances, there is a service on [[https://juliabox.com|JuliaBox.com]] that allows to run Julia/JuMP scripts for free in the browser, without anything to install on the local computer.   +That's said, for people that don't need such flexibility, the package automatically install a local pre-compiled version of the solver, so just adding the package relative to the solver is enough to start writing the model.   
  
  
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 ==== Importing the libraries ==== ==== Importing the libraries ====
  
-You will need to import as a minima the ''JuMP'' module. If you wish to specify a solver engine rather than letting JuMP select a suitable one, you will need to import also the module relative to the solver, e.g. ''Ipopt'' or  ''GLPKMathProgInterface''+You will need to import as a minima the ''JuMP'' module and a suitable solver. In this case the problem is linear, so we can use ''GLPK'' (''HiGHS'' is another popular alternative). If the problem would have been non-linear, you could have used the ''Ipopt'' solver/package
  
 <code  julia> <code  julia>
-# Import of the JuMP and DataFrames modules (the latter one just to import the data from a header based table, as in the original trasnport example in GAMS  +# Import of the JuMP, GLPK, CSV and DataFrames modules (the latter twos just to import the data from a header based table, as in the original trasnport example in GAMS  
-using JuMP, DataFrames+using CSV, DataFrames, GLPK, JuMP
 </code> </code>
  
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 #      seattle          2.5           1.7          1.8 #      seattle          2.5           1.7          1.8
 #      san-diego        2.5           1.8          1.4  ; #      san-diego        2.5           1.8          1.4  ;
-d_table = wsv"""+d_table = CSV.read(IOBuffer("""
 plants     new_york  chicago  topeka plants     new_york  chicago  topeka
 seattle    2.5       1.7      1.8 seattle    2.5       1.7      1.8
 san_diego  2.5       1.8      1.4 san_diego  2.5       1.8      1.4
-"""+"""), DataFrame, delim=" ", ignorerepeated=true,copycols=true)
 d = Dict( (r[:plants],m) => r[Symbol(m)] for r in eachrow(d_table), m in markets) d = Dict( (r[:plants],m) => r[Symbol(m)] for r in eachrow(d_table), m in markets)
 # Here we are converting the table in a "(plant, market) => distance" dictionary # Here we are converting the table in a "(plant, market) => distance" dictionary
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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 ''Model()'' call.\\ 
-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 options with the ''set_optimizer_attribute'' function, e.g.: 
-''mymodel = Model(solver=IpoptSolver(print_level=0))''+''set_optimizer_attribute(trmodel, "msg_lev", GLPK.GLP_MSG_ON)''
  
 <code julia> <code julia>
 # Model declaration (transport model) # Model declaration (transport model)
-trmodel = Model()  +trmodel = Model(GLPK.Optimizer) 
 </code> </code>
  
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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 (":Optimal" if all went fine)+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 (''MOI.OPTIMAL'' if all went fine)
  
 <code julia> <code julia>
-status = solve(trmodel)+optimize!(trmodel) 
 +status = termination_status(trmodel)
 </code> </code>
  
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 <code julia> <code julia>
-if status == :Optimal +if status == MOI.OPTIMAL 
-    println("Objective value: ", getobjectivevalue(trmodel)) +    println("Objective value: ", objective_value(trmodel)) 
-    println(getvalue(x))+    println("Shipped quantities: ") 
 +    println(value.(x))
     println("Shadow prices of supply:")     println("Shadow prices of supply:")
-    [println("$p = $(getdual(supply[p]))") for p in plants]+    [println("$p = $(dual(supply[p]))") for p in plants]
     println("Shadow prices of demand:")     println("Shadow prices of demand:")
-    [println("$m = $(getdual(demand[m]))") for m in markets]+    [println("$m = $(dual(demand[m]))") for m in markets] 
 + 
 else else
     println("Model didn't solved")     println("Model didn't solved")
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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 ''julia transport.jl''.+If you are using instead the Julia terminal,  you can run the script as ''julia transport.jl''.
  
 ===== Further help ===== ===== Further help =====
-Documentation of JuMP is available from [[https://jump.readthedocs.io/en/latest/|this page]]. However if you want to do serious things with juMP, it is most likely that you will have to either look at the source code or consult the [[https://discourse.julialang.org/c/domain/opt|discussion forum]].+Documentation of JuMP is available from [[https://jump.dev/|this page]], and community-based support is available on [[https://discourse.julialang.org/c/domain/opt|the Discourse forum]].
  
 Happy modelling with JuMP ;-) Happy modelling with JuMP ;-)
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