In the following example, I get a feasible solution with cbc (the command line version cbcSolver) which is suboptimal. If I use the minimum.cpp call instead, cbc does find the optimum, however takes significantly longer time.
The example consists of the following files: (1) example.mps : the problem definition (2) example_cbc_found1.txt : the solution found by cbc (3) example_cbc_found.txt : again the same solution in a different format (sparse matrix format where line 1 contains numrows numcols 0, and each further line contains i j A[i,j]) (4) example_optimum.txt : the optimum in sparse matrix format (5) otherexample.mps : a very similar (even slightly larger) example where cbc does find the global optimum
I get the problem with different versions of cbc, e.g. 2.3.2. and 2.4.1. Many thanks for looking into this! Jan.
Attachment: https://github.com/s-c-e/cbc-trac-migration-attachments/blob/master/trac-ticket-89.zip
In the following example, I get a feasible solution with cbc (the command line version cbcSolver) which is suboptimal. If I use the minimum.cpp call instead, cbc does find the optimum, however takes significantly longer time.
The example consists of the following files: (1) example.mps : the problem definition (2) example_cbc_found1.txt : the solution found by cbc (3) example_cbc_found.txt : again the same solution in a different format (sparse matrix format where line 1 contains numrows numcols 0, and each further line contains i j A[i,j]) (4) example_optimum.txt : the optimum in sparse matrix format (5) otherexample.mps : a very similar (even slightly larger) example where cbc does find the global optimum
I get the problem with different versions of cbc, e.g. 2.3.2. and 2.4.1. Many thanks for looking into this! Jan.