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--- personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:12]
antonello [Complete script]
+++ personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:20]
antonello [Definition of the "parameters"]
@@ Line 31: / Line 31: @@
 Run, only once, the following code to install JuMP language and a couple of open source solvers:
 <code julia>
-Pkg.update()                        # To refresh the list of newest packages
+using Pkg               # Load the package manager
-Pkg.add("JuMP")                     # The mathematical optimisation library
+Pkg.update()            # To refresh the list of newest packages
-Pkg.add("GLPKMathProgInterface")    # A lineaqr and MIP solver
+Pkg.add("CSV")          # A library to work with Comma Separated Values
-Pkg.add("Ipopt")                    # A non-linear solver
+Pkg.add("DataFrames")   # A library to deal with dataframes (R like tabular data)
-Pkg.add("DataFrames")               # A library to deal with dataframes (R like tabular data)
+Pkg.add("JuMP")         # The mathematical optimisation library
+Pkg.add("GLPK")         # A linear and MIP solver
+Pkg.add("Ipopt")        # A non-linear solver (not needed in this example)
 </code>
@@ Line 42: / Line 44: @@
 ==== 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>
-# 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>
@@ Line 94: / Line 96: @@
 #      seattle          2.5           1.7          1.8
 #      san-diego        2.5           1.8          1.4  ;
-d_table = wsv"""
+d_table = CSV.read(IOBuffer("""
 plants     new_york  chicago  topeka
 seattle    2.5       1.7      1.8
 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)
 # Here we are converting the table in a "(plant, market) => distance" dictionary