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Target Cuts from Relaxed Decision Diagrams

This is the implementation used to produce the computational results in the following paper:

In this paper and implementation, we generate target cuts from relaxed decision diagrams, using as benchmarks the independent set problem and the set covering problem. In addition, this code supports general pure binary problems in MPS format. In a nutshell, we generate cutting planes for an integer programming model by compactly encoding a relaxation of the feasible set using a decision diagram and extracting valid inequalities for this relaxation.

The main requirements are CPLEX 12.6 and a C++ compiler in a Linux environment. Python 2.7 with numpy and matplotlib is required to process data and plot graphs. Boost is used but included with the code.

Please be aware that the code quality is at research level and this repository is not expected to be maintained. Its main purpose is to allow and facilitate the reproducibility of the results contained in the above paper. However, you are free to adapt it to your own purposes; see for more details.

Along with the code and the scripts for reproducing the experiments, we include instances and abridged output files from the runs done for the paper. Full output files are not included as they are too large.

Instructions for reproducing the experiments are below. For information about the options supported by the program, see For information about the plots that can be reproduced with this code, see


In order to reproduce the results, you need to perform the following three steps after downloading the package and unpacking it:

  1. Compile the code;
  2. Run the test scripts; and
  3. Run the plotting scripts.

The test scripts are Bash scripts that run all experiments involved in the paper (optionally in parallel). The plotting scripts are Python scripts that process the output and plot the graphs shown in the paper.

You should unpack the package in the machine you will run the experiments; however, the plotting scripts support remote access via ssh and can be used in a different machine. See further below for more details.

Compiling the code

The code is in the directory ddopt. To compile it, follow these steps:

  1. Ensure you have all dependencies: CPLEX 12.6 (other versions may work) and a C++ compiler. Boost is used but it is included in ddopt/deps/boost_deps.tar.gz, which is automatically unpacked by the Makefile. The boost license file is within the package.

  2. Configure the CPLEX path by opening the Makefile file and setting the variable BASEILOGDIR to the path where CPLEX is installed in your machine.

  3. Run make to compile the code.

Running the test scripts

The test scripts are in the directory tests. You must have compiled the code to perform this step. The Makefile will automatically copy the binary to the tests/bin directory, which will be used by the test scripts.

Note: If you want to run a smaller test, run ./ in order to keep only the first 3 numbered instances.

  1. To run the independent set experiments, run ./ [n], where n is the number of processes that will be run. The parallelization is simple: we assign one process per instance number. Since for independent set there are 10 instance numbers, we recommend setting n to a divisor of 10.

  2. To run the set covering experiments, run ./ [n], where n is the number of processes that will be run. Similarly to above, we recommend setting n to a divisor of 16 as there are 16 instance numbers for set covering.

Running the plotting scripts

The plotting scripts are in the directory tests. You must have run the experiments to completion to perform this step, which may take days. However, these scripts also work with the abridged output files included in the package. The scripts require numpy, matplotlib, and optionally paramiko if used remotely.

  1. Configure the plotting settings. These are at the top of and there are four options that can be configured:

    • USERHOST: If the files are available locally, leave this as is (as an empty string ""). If the files are in a remote server, this is the address of the server in the format "[username]@[hostname]". The server is accessed via ssh (port 22).

    • PATH: This is the path of the output files, whether locally or on a server.

    • OUTPUT_DIR: This is the directory where the plots will be created.

    • PLOT_COLOR: Set this to True if plots should be colored.

    Additionally, if you are plotting gap closed for small set cover instances, copy the .opt files from the instances/setcover directory to the output/setcover_gap output directory.

  2. Run python all.

This will process the output files and generate all plots. Specific plots can be generated by providing different arguments; run python without arguments to obtain a list.

Other files

Other than the code and the scripts included above, we include:

  • Instances: The directories tests/instances/indepset and tests/instances/setcover respectively contain the independent set and set covering instances used for the experiments.

    • Instance generation: The directory tests/instances/scripts includes the Python scripts used to generate instances. The set covering script requires CPLEX and by default also generates .opt files with the optimal value for bandwidth 40 instances. The script runs the program to write exact decision diagram widths to .width files for the files provided as arguments. These .opt (for set covering) and .width files are necessary for tests with the smaller instances.
  • Abridged output files: The package tests/abridged_output.tar.gz in the root directory contains shortened versions of the output files that were used for plotting the graphs in the paper. The full output is not included due to their size (> 2GB). These output files were created by keeping only the lines parsed by the plotting scripts. This means that these files are not very human-readable; however, they can be used with the plotting scripts to generate the plots from the paper.

  • Affine independence verifier: The affine independence property of the points provided by flow decomposition is proven in the paper. We use the external Python script in the tests directory to assert that this holds true.


This implementation has its origins in the code from the following paper:

It was heavily modified since then. Their original code is available here.


Implementation for the paper "Target Cuts from Relaxed Decision Diagrams"







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