Commit f0eae78b authored by Mario Surlemont's avatar Mario Surlemont
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Update todo.md

parent 6c0419fa
...@@ -53,7 +53,8 @@ ...@@ -53,7 +53,8 @@
- A thing which was interesting that for their specific problems (which were not really close to MCDS and had only connectivity in common) they achived a much stronger LP bound. - A thing which was interesting that for their specific problems (which were not really close to MCDS and had only connectivity in common) they achived a much stronger LP bound.
- They added cuts before an ILP solution was found and added cuts for LP solutions also. - They added cuts before an ILP solution was found and added cuts for LP solutions also.
* Read _An Efficient Branch and Cut Algorithm to Find Frequently Mutated Subnetworks in Cancer_ again with focus on symmetry breaking. * Read _An Efficient Branch and Cut Algorithm to Find Frequently Mutated Subnetworks in Cancer_ again with focus on symmetry breaking.
* Read through _An Integer Programming Approach for Fault-Tolerant Connected Dominating Sets*_ again and check for symmetry breaking or other constraints to tighten up the space of solutions. * Read through _An Integer Programming Approach for Fault-Tolerant Connected Dominating Sets*_ again and check for symmetry breaking or other constraints to tighten up the space of solutions. ✔
- I could not find anything about symmetry breaking or additional inequalities for the case k=d=1 (Which is standard MCDS). But the table of results was interesting because they also tested their implemenation for the case k=d=1 which then is equal to our ILP-formulation. Their results were not bad but unfortunately I could not find anything more detailed description of their test graphs. Only number of nodes and density is shown. But those two properties are not sufficient as my personal test on random graphs revealed.
# Backlog # Backlog
## Python ## Python
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