Set-up a desired installation path for SCIP / SoPlex (e.g., /opt/scip
):
export SCIPOPTDIR='/opt/scip'
SoPlex 4.0.1 (free for academic uses)
https://soplex.zib.de/download.php?fname=soplex-4.0.1.tgz
tar -xzf soplex-4.0.1.tgz
cd soplex-4.0.1/
mkdir build
cmake -S . -B build -DCMAKE_INSTALL_PREFIX=$SCIPOPTDIR
make -C ./build -j 4
make -C ./build install
cd ..
SCIP 6.0.1 (free for academic uses)
https://scip.zib.de/download.php?fname=scip-6.0.1.tgz
tar -xzf scip-6.0.1.tgz
cd scip-6.0.1/
Apply patch file in learn2branch/scip_patch/
patch -p1 < ../learn2branch/scip_patch/vanillafullstrong.patch
mkdir build
cmake -S . -B build -DSOPLEX_DIR=$SCIPOPTDIR -DCMAKE_INSTALL_PREFIX=$SCIPOPTDIR
make -C ./build -j 4
make -C ./build install
cd ..
For reference, original installation instructions here.
Recommended setup: conda + python 3
https://docs.conda.io/en/latest/miniconda.html
Required to compile PySCIPOpt and PySVMRank
conda install cython
SCIP's python interface (modified version)
pip install git+https://github.com/ds4dm/PySCIPOpt.git@ml-branching
conda install scikit-learn=0.20.2 # ExtraTrees
pip install git+https://github.com/jma127/pyltr@78fa0ebfef67d6594b8415aa5c6136e30a5e3395 # LambdaMART
git clone https://github.com/ds4dm/PySVMRank.git
cd PySVMRank
wget http://download.joachims.org/svm_rank/current/svm_rank.tar.gz # get SVMrank original source code
mkdir src/c
tar -xzf svm_rank.tar.gz -C src/c
pip install .
conda install tensorflow-gpu=1.12.0