xfac is a c++ library (with python binding) to learn low-rank tensor train representations from a given tensor or function. The learning is made by tensor cross interpolation. Given a multidimensional function $f:\mathcal{R}^n \rightarrow \mathcal{C}$, the library can generate an accurate approximation:

$$ f(x_1,x_2,...,x_n) \approx M_1(x_1)M_2(x_2)...M_n(x_n) $$

where $M$ are matrices. As the variables are effectively separated, some tasks like integration or sum are now very cheap.

This factorization can be relevant even for function of one variable via the so-called quantics representation. In quantics, each variable is replaced by its $R$ binary digits $\sigma_0,\sigma_1,...,\sigma_{R-1}$ (for instance $x=\sum^{R-1}_{r=0}\sigma_r 2^{-r}$ if $0 \le x < 1$), and then factorized:

$$ F(\sigma_{0}\sigma_{1}...\sigma_{R-1}) \equiv f(x(\bar{\sigma})) \approx M_1(\sigma_{0})M_2(\sigma_1)...M_n(\sigma_{R-1}) $$

making explicit the possible low entanglement between different scales of the function. Potentially, quantics allows arbitrary resolution representation (by using astronomically big grids of $2^R$ points) with cost scaling linear in $R$.

As examples of applications we can mention:

1. Integration of multidimensional functions:
   • quadratures
   • quantics
2. Computation of partition functions
3. Quantics: superfast Fourier transform
4. Quantics: solving partial differential equations
5. Automatic construction of matrix product operators
6. Function optimization
7. Quantum chemistry

The orignal math papers and the implemented algorithms are referenced here and:

Yuriel Núñez Fernández, Marc K. Ritter, Matthieu Jeannin, Jheng-Wei Li, Thomas Kloss, Olivier Parcollet, Jan von Delft, Hiroshi Shinaoka, and Xavier Waintal, Learning low-rank tensor train representations: new algorithms and libraries, in preparation, (2024).

• API documentation and tutorials are kindly hosted at https://xfac.readthedocs.io/en/latest
• You can also read the code because is not big and includes comments :-)
• The documentation can be generated locally.

See folder example (for c++) and notebook (for python).

The dependencies are automatically satisfied using git submodule. They are

• armadillo for linear algebra on matrix and 3-leg tensor. Armadillo depends on blas, lapack.
• Catch2 for testing.
• pybind11 for python interface.
• carma to convert armadillo objects to python numpy array.

git clone https://github.com/tensor4all/xfac.git
cd xfac
cmake -S . -B build -D CMAKE_BUILD_TYPE=Release

If you want:

• To compile the tests, add -D XFAC_BUILD_TEST=ON to the cmake line above.

• To generate the python library, add -D XFAC_BUILD_PYTHON=ON

• To install the library in specific local directory, add -D CMAKE_INSTALL_PREFIX=<my_local_install_directory>

To compile and install (respectively):

cmake --build build -j 4
cmake --build build --target install

the last line is optional and may need admin rights (sudo) if no local directory was provided.

Compilation under macOS has been tested using the gcc compiler. gcc itself can be installed with Homebrew. To build xfac using this compiler, the two environment variables CC and CXX must point to gcc's C and respectively C++ compiler binary. Having for instance gcc version 13 installed, one typically needs to add

export CC=/usr/local/bin/gcc-13
export CXX=/usr/local/bin/g++-13

to the ~/.zshrc script. Above compiler path is the standard path where gcc is usually found under macOS. If gcc is installed in a different location or has a different version, the path must be modified accordingly.

Moreover, the git submodule mechanism should ensure that the other dependecies are satisfied automatically. Otherwise, if one likes to use own libraries, one can point to them as well in the ~/.zshrc script. For Armadillo for instance, this would look like

export PATH=YOUR_PATH_TO_ARMADILLOD/include:$PATH

where YOUR_PATH_TO_ARMADILLOD must be set to the correct path.

A simple example like integral.cpp at example/integral/ can be compiled directly (after building xfac):

g++ -O3 integral.cpp -std=c++17 -L../../build -lxfac -L../../build/extern/arma -larmadillo -I../../include

by manually specifying the path to xfac and armadillo libraries.

If you use cmake for your project, then xfac can be used after building without exporting any path (hopefully), by adding this to your CMakeLists.txt

find_package(xfac REQUIRED)
target_link_libraries(myTarget xfac::xfac)

If you experience any problem, you can try the above combined with a later call to:

cmake <my_stuff> -D CMAKE_INSTALL_PREFIX=<my_local_directory>

where <my_local_directory> can be <my_path_to_xfac>/build (then you are linking the built library) or <my_local_install_directory> in case you installed xfac on a local directory.

Alternatively, you can manually copy the xfac source code inside your project and add xfac as subproject in your CMakeLists.txt:

add_directory(xfac)
target_link_library(myTarget xfac::xfac)

Notice that the manual copy can be automatized by adding xfac as a git submodule or using cmake FetchContent.

If you have problems using the Jupyter notebook with the conda environment, see this PR#7.

The documentation can be build locally by running the commands given below from inside the docs/ folder.

• First, the Doxygen documentation can be generate by typing:

doxygen

The output is generated in folder doxygen_out/, and includes the html version (index.html) and also a latex version (after make you will find refman.pdf). The Doxygen documentation is kind of verbose and typically only an intermediate step for building the documetation via Jupyter in the second step.

• Second, the Jupyter Book documentation can be build by running the command

jupyter-book build ./

after running the first step with Doxygen. The generated HTML documentation can be viewed by opening the file _build/html/index.html with a webbrowser. Note that this documentation is similar to xfac's online documentation, containing a more readable version of the C++ API documentation and also the tutorial examples. The required Python packages to build the documentation in the second step can be installed by running:

pip3 install -r requirements.txt