PyLBFGS is a Python 3 wrapper of the libLBFGS library -- a C port (written by Naoaki Okazaki) of the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm (written in Fortran by Jorge Nocedal).

At this time, only the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) algorithm is exposed (although very little work would be required to expose the full libLBFGS implementation).

Also note that PyLBFGS has only been compiled and tested with Python 3 on a 64-bit Ubuntu machine. It should work in other environments... but tweaking the installation commands is a task left to the reader.


  1. Download libLBFGS source code and install (see its README for extra info):

    sudo apt install libtool automake virtualenv
    ./configure --enable-sse2
    sudo make install
  2. Download or clone the PyLBFGS project:

    git clone
    cd pylbfgs
  3. Setup and activate a virtual environment (modify the Python version if necessary):

    virtualenv -p python3.6 --prompt="(pylbfgs) " .venv
    . .venv/bin/activate
  4. Install the project:

    python install
  5. Alternatively, build in-place:

    python build_ext -i

Basic Use

Import the OWL-QN algorithm:

from pylbfgs import owlqn

Define an evaluation callback to provide the objective function and gradient evaluations:

def evaluate(x, g, step):
    # The algorithm calls this callback to obtain the values of the
    # objective function and its gradients when needed. Store the
    # gradients into the numpy array g. Return the objective function
    # evaluated at x.
    return 0.0

Optionally, define a callback to receive the progress of the optimization process:

def progress(x, g, fx, xnorm, gnorm, step, k, ls):
    # Print variables to screen or file or whatever. Return zero to 
    # continue algorithm; non-zero will halt execution.
    return 0

Run the OWL-QN algorithm. The result will be the minimum in vector form.

orthantwise_c = 5
x = owlqn(size_of_x, evaluate, progress, orthantwise_c)

If you want to use the built-in progress callback, just pass None.

Refer to the libLBFGS API for more info about what each of the callback arguments mean.


See the script for an advanced example in which we use compressed sensing to reconstruct a sparsely sampled image. A more thorough example of compressed sensing (using PyLBFGS) can be found on my weblog here.

pip install Pillow==5.0.0 scipy==1.0.0 matplotlib==2.1.2

Author & License

PyLBFGS was written by Robert Taylor and is licensed under the MIT license.