FICO Xpress Optimization Examples Repository: Mosel files for the Mosel-Python comparison blog

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Mosel files for the Mosel-Python comparison blog

Description

Mosel files for the blog post comparing Mosel and Python.

Instructions for running these files:

Extract the data files into the same directory as the Mosel files
To run a single file via the command line, call Mosel and pass the filename followed by "DATA_FILE_PREFIX=" and the first 2 digits of the data file. For example: mosel SparseGrouping_std.mos DATA_FILE_PREFIX=00

Further explanation of this example: See the blog 4 Main Takeaways from Comparing Xpress Mosel and Python for Optimization Models

moselblog.zip

[download all files]

Source Files

By clicking on a file name, a preview is opened at the bottom of this page.

ComplexConstraints_std.mos	[download]
ComplexConstraints_adv.mos	[download]
MatrixVariables_std.mos	[download]
MatrixVariables_adv.mos	[download]
ProductsMachines_std.mos	[download]
ProductsMachines_adv.mos	[download]
QuantityDiscount_std.mos	[download]
QuantityDiscount_adv.mos	[download]
SparseGrouping_std.mos	[download]
SparseGrouping_adv.mos	[download]
SparseVariables_std.mos	[download]
SparseVariables_adv.mos	[download]
TSP_std.mos	[download]
TSP_adv.mos	[download]

Data Files

00_H_MatrixVariables_V.csv	[download]
00_H_ProductsMachines_T.csv	[download]
00_H_TSP_cities.csv	[download]
00_H_ComplexConstraints_D.csv	[download]
00_H_ProductsMachines_K.csv	[download]
00_H_QuantityDiscount_V.csv	[download]
00_H_SparseVariables_C.csv	[download]
00_H_MatrixVariables_S.csv	[download]
00_H_SparseGrouping_C.csv	[download]
00_H_ProductsMachines_U.csv	[download]
00_H_TSP_tours.csv	[download]
ComplexConstraints.tar.gz	[download]
MatrixVariables.tar.gz	[download]
ProductsMachines.tar.gz	[download]
QuantityDiscount.tar.gz	[download]
SparseGrouping.tar.gz	[download]
SparseVariables.tar.gz	[download]
TSP.tar.gz	[download]
03_H_ProductsMachines_K.tar.gz	[download]
04_H_QuantityDiscount_V.tar.gz	[download]

00_H_MatrixVariables_V.csv

P,V
1,140
2,120
3,40
4,30
5,80
6,100
7,20
8,140
9,60
10,30
11,140
12,110

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