MADNESS  0.10.1
Classes | Namespaces | Functions
tensortrain.h File Reference

Defines and implements the tensor train decomposition as described in I.V. Oseledets, Siam J. Sci. Comput. 33, 2295 (2011). More...

#include <madness/tensor/tensor.h>
#include <madness/tensor/srconf.h>
#include <madness/tensor/clapack.h>
#include <madness/tensor/tensor_lapack.h>
#include <madness/fortran_ctypes.h>
#include <madness/world/archive.h>
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Classes

class  madness::TensorTrain< T >
 

Namespaces

 madness
 File holds all helper structures necessary for the CC_Operator and CC2 class.
 

Functions

template<class T , class Q >
TensorTrain< TENSOR_RESULT_TYPE(T, Q)> madness::apply (const TensorTrain< T > &op, const TensorTrain< Q > &t, const double thresh)
 apply an operator in TT format on a tensor in TT format More...
 
template<class T , class Q >
TensorTrain< TENSOR_RESULT_TYPE(T, Q)> madness::general_transform (const TensorTrain< T > &t, const Tensor< Q > c[])
 Transform all dimensions of the tensor t by distinct matrices c. More...
 
template<class T , class Q >
TensorTrain< TENSOR_RESULT_TYPE(T, Q)> madness::outer (const TensorTrain< T > &t1, const TensorTrain< Q > &t2)
 computes the outer product of two tensors More...
 
template<typename T >
long madness::rank_revealing_decompose (Tensor< T > &A, Tensor< T > &U, const double thresh, Tensor< typename Tensor< T >::scalar_type > &s, Tensor< T > &scr)
 
template<class T , class Q >
TensorTrain< TENSOR_RESULT_TYPE(T, Q)> madness::transform (const TensorTrain< T > &t, const Tensor< Q > &c)
 transform each dimension with the same operator matrix More...
 
template<class T , class Q >
TensorTrain< TENSOR_RESULT_TYPE(T, Q)> madness::transform_dir (const TensorTrain< T > &t, const Tensor< Q > &c, const int axis)
 Transforms one dimension of the tensor t by the matrix c, returns new contiguous tensor. More...
 
template<typename T >
TensorTrain< Tmadness::tt_identity (const long ndim, const long k)
 compute the n-D identity operator with k elements per dimension More...
 

Detailed Description

Defines and implements the tensor train decomposition as described in I.V. Oseledets, Siam J. Sci. Comput. 33, 2295 (2011).