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_ComplexConstraints_D.csv

RN1,RN2,D
0,0,0.60425
1,0,0.20564
2,0,0.11955
3,0,0.13296
4,0,0.36902
5,0,0.3871
6,0,0.62693
7,0,0.42457
0,1,0.48679
1,1,0.42482
2,1,0.48601
3,1,0.62485
4,1,0.00545
5,1,0.06919
6,1,0.19416
7,1,0.42667
0,2,0.52638
1,2,0.56691
2,2,0.62702
3,2,0.4704
4,2,0.41761
5,2,0.46634
6,2,0.30394
7,2,0.21163
0,3,0.28577
1,3,0.4743
2,3,0.64611
3,3,0.43997
4,3,0.51403
5,3,0.11109
6,3,0.01745
7,3,0.52024
0,4,0.58742
1,4,0.01604
2,4,0.31964
3,4,0.34207
4,4,0.38764
5,4,0.03377
6,4,0.58181
7,4,0.47337
0,5,0.53193
1,5,0.32514
2,5,0.52662
3,5,0.06238
4,5,0.14232
5,5,0.16817
6,5,0.30427
7,5,0.29859
0,6,0.46118
1,6,0.11573
2,6,0.34544
3,6,0.10903
4,6,0.49973
5,6,0.60331
6,6,0.39617
7,6,0.09762
0,7,0.31826
1,7,0.24527
2,7,0.55159
3,7,0.59221
4,7,0.2495
5,7,0.20507
6,7,0.36946
7,7,0.12208