Users need to install the following packages and add them to specific paths according to your CMakeLists.txt file.

1. cmake version 3.12 or greater
2. Basic matrix library, Armadillo: http://arma.sourceforge.net/
3. Intel Math Kernel Library (MKL): https://software.intel.com/en-us/mkl
4. Fastest Fourier Transform in the West (FFTW): http://www.fftw.org/
   (Optional) OpenMP

We tested our code on Ubuntu Linux.

CP decomposition is realized in cp_als.cpp file.
You can call cp_als function like:

    cp_mats A = cp_als(Tensor<T> a, rank r);    

The struct type is defined as:

template<class T>
struct cp_mats{
Mat<T> A,B,C;
};

where matrix A,B and C are the corresponding factors.

The type <T> can be float and double, you can run the test.cpp file to test the algorithm.

Tucker decomposition is realized in cp_als.cpp file.
You can call hosvd function like:

    tucker_core A = hosvd(Tensor<T> a, rank r);    

The struct type tucker_core is defined as:

template<class T>
struct tucker_core{
Mat<T> u1, u2, u3;
};

where matrix A,B and C are the corresponding factors.

The type <T> can be float and double, you can run the test.cpp file to test the algorithm.

You can simply run main function in T-SVD directory to test T-SVD algorithm. The cpp file for T-SVD, like cp_als.cpp, will be provided in the near furture.

For more API details, please refer to the tensor.h file, where all definitations and corresponding illustrations is provied therein. The corresponding functions is realized in tensor.cpp file.

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