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NeurIPS_BranchAndLearn

This repository is the official implementation of the paper: Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize

Download and extract the datasets.

MCFP_BAL: This is the package for the minimum cost flow problem with B&L. The training process and the test process are divided in two files. Please use the following commands:

  1. compile the program: g++ -std=c++11 train.cpp -o train / g++ -std=c++11 test.cpp -o test
  2. conduct training: ./train [graph file] [train data] [test data] [result file] [flow] [iteration] (for example: ./train data/graph_USA_100.txt data/train_USA_100.txt data/test_USA_100.txt data/BAL_USA_100.txt 20 3)
  3. conduct testing: ./test [graph file] [test data] [result file] [flow] (for example: ./test data/graph_USA_100.txt data/BAL_USA_100.txt result.txt 20)

MCVC_BAL: This is the package for the minimum cost vertex cover problem with B&L. The training process and the test process are divided in two files. Please use the following commands:

  1. compile the program: g++ -std=c++11 train.cpp -o train / g++ -std=c++11 test.cpp -o test
  2. conduct training: ./train [graph file] [train data] [test data] [prediction file] [iteration] [runtime file] (for example: ./train data/edge_POLSKA.txt data/train_POLSKA_100.txt data/test_POLSKA_100.txt BAL/BAL_POLSKA_100.txt 1 runtime/runtime.txt)
  3. conduct testing: ./test [graph file] [predicted data] [result file] (for example: ./test data/edge_POLSKA.txt BAL/BAL_POLSKA_100.txt result.txt)

MSS_BAL: This is the package for the multi-stage scheduling problem with B&L. The training process and the test process are divided in two files. Please use the following commands:

  1. compile the program: g++ -std=c++11 train.cpp -o train / g++ -std=c++11 test.cpp -o test
  2. conduct training: ./train [train data] [test data] [prediction file] [iteration] [runtime file] (for example: ./train data/train_jobNum10_100.txt data/test_jobNum10_100.txt BAL/BAL_jobNum10_100.txt 1 runtime/runtime.txt)
  3. conduct testing: ./test [predicted data] [result file] (for example: ./test BAL/BAL_jobNum10_100.txt result.txt)

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