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

FICO Xpress Optimization Examples Repository

Python files for the Mosel-Python comparison blog

Description

Python files for the blog post comparing Mosel and Python: 4 Main Takeaways from Comparing Xpress Mosel and Python for Optimization Models.

Instructions for running these files:

Extract the data files into the same directory as the Python files.
To run a single file via the command line, call Python and pass the filename followed by the first 2 digits of the data file. For example: python SparseGrouping_std.py 00

pythonblog.zip

[download all files]

Source Files

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

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