FALCONN 1.3.1
A library for similarity search over high-dimensional data based on Locality-Sensitive Hashing (LSH)
License: MIT
Keywords: nearest neighbor search similarity lsh locality-sensitive hashing cosine distance euclidean

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FALCONN - FAst Lookups of Cosine and Other Nearest Neighbors
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

FALCONN is a library with algorithms for the nearest neighbor search
problem. The algorithms in FALCONN are based on `Locality-Sensitive
Hashing <https://en.wikipedia.org/wiki/Locality-sensitive_hashing>`__
(LSH), which is a popular class of methods for nearest neighbor search
in high-dimensional spaces. The goal of FALCONN is to provide very
efficient and well-tested implementations of LSH-based data structures.

Currently, FALCONN supports two LSH families for the `cosine
similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`__:
hyperplane LSH and cross polytope LSH. Both hash families are
implemented with multi-probe LSH in order to minimize memory usage.
Moreover, FALCONN is optimized for both dense and sparse data. Despite
being designed for the cosine similarity, FALCONN can often be used for
nearest neighbor search under the Euclidean distance or a maximum inner
product search.

FALCONN is written in C++ and consists of several modular core classes
with a convenient wrapper around them. Many mathematical operations in
FALCONN are vectorized through the
`Eigen <http://eigen.tuxfamily.org/index.php?title=Main_Page>`__ and
`FFHT <https://github.com/FALCONN-LIB/FFHT>`__ libraries. The core
classes of FALCONN rely on
`templates <https://en.wikipedia.org/wiki/Andrei_Alexandrescu>`__ in
order to avoid runtime overhead.

How to use FALCONN
~~~~~~~~~~~~~~~~~~

We provide a C++ interface for FALCONN as well as a
`Python <https://www.python.org/>`__ wrapper (that uses
`NumPy <http://www.numpy.org/>`__). In the future, we plan to support
more programming languages such as `Julia <http://julialang.org/>`__.
For C++, FALCONN is a header-only library and has no dependencies
besides Eigen (which is also header-only), so FALCONN is easy to set up.
For further details, please see our
`documentation <https://github.com/falconn-lib/falconn/wiki>`__.

How fast is FALCONN?
~~~~~~~~~~~~~~~~~~~~

On data sets with about 1 million points in around 100 dimensions,
FALCONN typically requires a few milliseconds per query (running on a
reasonably modern desktop CPU).

For more detailed results, see
`ann-benchmarks <https://github.com/erikbern/ann-benchmarks>`__ of `Erik
Bernhardsson <https://erikbern.com/>`__. Let us point out that FALCONN
is especially competitive, when the RAM budget is quite restrictive,
which is not the regime the above benchmarks use.

Questions
~~~~~~~~~

Maybe your question is already answered in our `Frequently Asked
Questions <https://github.com/falconn-lib/falconn/wiki/FAQ>`__. If you
have additional questions about using FALCONN, we would be happy to
help. Please send an email to falconn.lib@gmail.com.

Authors
~~~~~~~

FALCONN is mainly developed by `Ilya
Razenshteyn <http://www.ilyaraz.org/>`__ and `Ludwig
Schmidt <http://people.csail.mit.edu/ludwigs/>`__. FALCONN has grown out
of a `research
project <http://papers.nips.cc/paper/5893-practical-and-optimal-lsh-for-angular-distance>`__
with our collaborators `Alexandr
Andoni <http://www.mit.edu/~andoni/>`__, `Piotr
Indyk <https://people.csail.mit.edu/indyk/>`__, and `Thijs
Laarhoven <http://thijs.com/>`__.

Many of the ideas used in FALCONN were proposed in research papers over
the past 20 years (see the
`documentation <https://github.com/FALCONN-LIB/FALCONN/wiki/Bibliography>`__).

If you want to cite FALCONN in a publication, here is the bibliographic
information of our research paper
`(bibtex) <http://papers.nips.cc/paper/5893-practical-and-optimal-lsh-for-angular-distance/bibtex>`__:

    `Practical and Optimal LSH for Angular
    Distance <http://papers.nips.cc/paper/5893-practical-and-optimal-lsh-for-angular-distance>`__
    Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn,
    Ludwig Schmidt NIPS 2015

License
~~~~~~~

FALCONN is available under the `MIT
License <https://opensource.org/licenses/MIT>`__ (see LICENSE.txt). Note
that the third-party libraries in the ``external/`` folder are
distributed under other open source licenses. The Eigen library is
licensed under the `MPL2 <https://www.mozilla.org/en-US/MPL/2.0/>`__.
The googletest and googlemock libraries are licensed under the `BSD
3-Clause License <https://opensource.org/licenses/BSD-3-Clause>`__. The
pybind11 library is licensed under a `BSD-style
license <https://github.com/pybind/pybind11/blob/master/LICENSE>`__.

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