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personal:blog:2017:0203_jump_for_gams_users [2018/06/18 15:11] 127.0.0.1 external edit |
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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Run, only once, the following code to install JuMP language and a couple of open source solvers: | Run, only once, the following code to install JuMP language and a couple of open source solvers: | ||
<code julia> | <code julia> | ||
- | Pkg.update() | + | using Pkg # Load the package manager |
- | Pkg.add(" | + | Pkg.update() |
- | Pkg.add(" | + | Pkg.add(" |
- | Pkg.add(" | + | Pkg.add(" |
- | Pkg.add(" | + | Pkg.add(" |
+ | Pkg.add(" | ||
+ | Pkg.add(" | ||
</ | </ | ||
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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 ;-) | ||
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<code Julia> | <code Julia> | ||
+ | # Transport example | ||
+ | |||
# Transposition in JuMP of the basic transport model used in the GAMS tutorial | # Transposition in JuMP of the basic transport model used in the GAMS tutorial | ||
# | # | ||
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# The Scientific Press, Redwood City, California, 1988. | # The Scientific Press, Redwood City, California, 1988. | ||
# - JuMP implementation: | # - JuMP implementation: | ||
- | + | ||
- | using JuMP, DataFrames | + | using CSV, DataFrames, GLPK, JuMP |
+ | |||
# Sets | # Sets | ||
plants | plants | ||
markets = [" | markets = [" | ||
+ | |||
# Parameters | # Parameters | ||
a = Dict( # capacity of plant i in cases | a = Dict( # capacity of plant i in cases | ||
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" | " | ||
) | ) | ||
+ | |||
# distance in thousands of miles | # distance in thousands of miles | ||
- | d_table = wsv""" | + | d_table = CSV.read(IOBuffer(""" |
plants | plants | ||
seattle | seattle | ||
san_diego | san_diego | ||
- | """ | + | """ |
d = Dict( (r[: | d = Dict( (r[: | ||
+ | |||
f = 90 # freight in dollars per case per thousand miles | f = 90 # freight in dollars per case per thousand miles | ||
+ | |||
c = Dict() # transport cost in thousands of dollars per case ; | c = Dict() # transport cost in thousands of dollars per case ; | ||
[ c[p,m] = f * d[p,m] / 1000 for p in plants, m in markets] | [ c[p,m] = f * d[p,m] / 1000 for p in plants, m in markets] | ||
+ | |||
# Model declaration | # Model declaration | ||
- | trmodel = Model() # transport model | + | trmodel = Model(GLPK.Optimizer) # transport model |
+ | |||
# Variables | # Variables | ||
@variables trmodel begin | @variables trmodel begin | ||
x[p in plants, m in markets] >= 0 # shipment quantities in cases | x[p in plants, m in markets] >= 0 # shipment quantities in cases | ||
end | end | ||
+ | |||
# Constraints | # Constraints | ||
@constraints trmodel begin | @constraints trmodel begin | ||
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sum(x[p,m] for p in plants) | sum(x[p,m] for p in plants) | ||
end | end | ||
+ | |||
# Objective | # Objective | ||
@objective trmodel Min begin | @objective trmodel Min begin | ||
sum(c[p, | sum(c[p, | ||
end | end | ||
+ | |||
print(trmodel) | print(trmodel) | ||
+ | |||
+ | optimize!(trmodel) | ||
+ | status = termination_status(trmodel) | ||
- | status = solve(trmodel) | + | if status == MOI.OPTIMAL |
- | + | println(" | |
- | if status == :Optimal | + | |
- | println(" | + | |
println(" | println(" | ||
- | println(getvalue(x)) | + | println(value.(x)) |
println(" | println(" | ||
- | [println(" | + | [println(" |
println(" | println(" | ||
- | [println(" | + | [println(" |
+ | |||
else | else | ||
println(" | println(" | ||
println(status) | println(status) | ||
end | end | ||
- | + | ||
# Expected result: | # Expected result: | ||
# obj= 153.675 | # obj= 153.675 |