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Computational results and tests of four formulations for the Optimal Communication Spanning Tree problem. We implemented three formulations: Path-based, flow-based, and rooted-based. Moreover, we consider the relaxation (in some variables) of the flow-based formulation. In this project, we adopt the Gurobi optimizer as the solver for the MILP fo…

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#Run Instances

First, compile CMAKE. Then a MAKEFILE. will be generated

CMAKE will build a target rule for creating a new set of instances in Directory Instances/< InstanceName>

cmake . -D InstanceOCSTP=<InstanceName>
make generate-instance

Then, if you want to run the experiments in < InstanceName> you should execute the following:

make run-instance

Observe that if you want to change the target directory, you must execute:

cmake . -D InstanceOCSTP=<OtherInstanceName> 

The format of the directory is specified in the python scripts (scripts/python). In particular, the files configOCST.py contains all the information and structure on running tests.

##About Source Code

Conversely, if you want to modify the c++ source code (in src/), you should execute CMAKE. (in the directory src/) and rerun the CMake command in the root directory.

cd src
cmake .
make

The binary generated is saved in the directory src/bin. Since we only make modifications in src/ we can execute make run-instance with an updated version without additional changes.

##Testing

Furthermore, if you want to add a new test, you must add the input and output file to Directory /src/test/TestInstances. Then you should modify the file formulations_test.cpp and include the file names to input and output. Then run CMake . in directory src to create and update binaries.

To execute test cases, you should execute (after cmake .):

ctest -VV 

Thank you.

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Computational results and tests of four formulations for the Optimal Communication Spanning Tree problem. We implemented three formulations: Path-based, flow-based, and rooted-based. Moreover, we consider the relaxation (in some variables) of the flow-based formulation. In this project, we adopt the Gurobi optimizer as the solver for the MILP fo…

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