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Convex optimization for traffic assignment


To run the MATLAB implementation, see MATLAB setup

Python dependencies:

sudo easy_install pip
pip install -r requirements.txt

Also needed is scipy. If you find some missing dependencies, please add them here.

To build the simplex projection c extension:

  1. cd into python/c_extensions
  2. run python2 build_ext --inplace

Set up the pre-commit hook (which automatically runs the fast unit tests):

  ln -s ../../ .git/hooks/pre-commit


First download CPLEX for your OS from the IBM website (you'll need to sign up for an academic initiative account first). It would be a file named CPLEX_xxxxxxxxxx.bin.

To install, run (using sudo if necessary):

chmod +x CPLEX_xxxxxxxxxx.bin

Note the installation directory, then to install the python bindings (using sudo if necessary, for OSX users you might need to run sudo -s first before these steps):

cd <installation-directory>/ILOG/CPLEX_Studio1261/cplex/python/2.7/x86-64_linux
python install

Install openopt (documentation here:

pip install openopt

To see an example on how to use CPLEX, look at tests/fast/

Running via Python

Run the python implementation from the traffic-estimation/python directory.

To run the main test, see these examples:

cd ~/traffic-estimation/python
python --file route_assignment_matrices_ntt.mat --log=DEBUG --solver LBFGS
python --file route_assignment_matrices_ntt.mat --log=DEBUG --solver BB
python --file route_assignment_matrices_ntt.mat --log=DEBUG --solver DORE

If the dataset you want to run is not in the data directory, symlink it in from the main dataset.

To run 3-fold cross validation test:

python --log=DEBUG

Running ISTTT

After generating the set of matrices run:

python --log=DEBUG --solver BB

MATLAB setup

MATLAB dependencies (must be run every time MATLAB is started):


c_extensions setup

To bind Cythonic extensions to Python

cd python/c_extensions
python build_ext --inplace

check if it created an executable '' and 'c_extensions.cpp'

Running via MATLAB

Run main.m.


Mark Schmidt's L1General, a set of Matlab routines for solving L1-regularization problems.


Block-simplex constrained least squares: a collection of optimization methods



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