#include <SVDTensor.h>

Inheritance diagram for madness::SVDTensor< T >:

[legend]

Collaboration diagram for madness::SVDTensor< T >:

[legend]

Public Member Functions
	SVDTensor ()
	SVDTensor (const long ndims, const long *dims)
	SVDTensor (const SRConf< T > &rhs)
	SVDTensor (const std::vector< long > &dims)
	SVDTensor (const SVDTensor< T > &rhs)=default
	SVDTensor (const Tensor< double > &weights, const Tensor< T > &vector1, const Tensor< T > &vector2, const long &ndim, const long *dims)
	SVDTensor (const Tensor< T > &rhs, const double eps)
void	divide_and_conquer_reduce (const double &thresh)
	reduce the rank using a divide-and-conquer approach
SVDTensor< T > &	emul (const SVDTensor< T > &other)
SVDTensor< T > &	gaxpy (T alpha, const SVDTensor< T > &rhs, T beta)
long	nCoeff () const
SVDTensor &	operator= (const T &number)
void	orthonormalize (const double &thresh)
void	orthonormalize_random (const double &thresh)
long	rank () const
void	recompute_from_range (const Tensor< T > &range)
	compute an SVD from a given matrix and its range
void	truncate_svd (const double &thresh)
Public Member Functions inherited from madness::SRConf< T >
	SRConf ()
	default ctor
	SRConf (const long &ndim, const long *dimensions, const long nci)
	SRConf (const long &ndim, const std::array< long, TENSOR_MAXDIM > &dimensions, const long nci)
	SRConf (const SRConf &rhs)=default
	copy ctor (tested); shallow copy
	SRConf (const Tensor< double > &weights, const std::vector< Tensor< T > > &vectors, const long &ndim, const long &dims, const long nci)
	ctor with provided weights and effective vectors; shallow copy
	SRConf (const Tensor< double > &weights, const tensorT &vector1, const tensorT &vector2, const long &ndim, const long *dims, const long nci)
	explicit ctor with two vectors (aka SVD), shallow
	~SRConf ()
	dtor
void	add_SVD (const SRConf< T > &rhs, const double &thresh)
	add two orthonormal configurations, yielding an optimal SVD decomposition
void	append (const SRConf< T > &rhs, const double fac=1.0)
	append configurations of rhs to this
void	append (const SRConf< T > &rhs, const double_complex fac=1.0)
const std::vector< Slice > &	c0 (const int idim) const
	return a Slice that corresponds the that part of vector_ that holds coefficients
void	clear ()
SRConf< T >	copy_slice (const std::array< Slice, TENSOR_MAXDIM > &s) const
	return a slice of this (deep copy)
int	dim_per_vector (int idim) const
	return the number of physical dimensions
SRConf< T > &	emul (const SRConf< T > &other)
	perform elementwise Hadamard product
Tensor< T >	flat_vector (const unsigned int &idim)
	return shallow copy of a slice of one of the vectors, flattened to (r,kVec)
const Tensor< T >	flat_vector (const unsigned int &idim) const
	return shallow copy of a slice of one of the vectors, flattened to (r,kVec)
Tensor< T >	flat_vector_with_weights (const int dim) const
	return flat (r,i) view of the tensor with the weights multiplied in
template<typename Q>
SRConf< TENSOR_RESULT_TYPE(T, Q)>	general_transform (const Tensor< Q > c[]) const
const SRConf	get_configs (const int &start, const int &end) const
bool	has_data () const
	does this have any data?
bool	has_no_data () const
	does this have any data?
bool	has_structure () const
	return if this has a tensor structure (has not been flattened)
long	kVec (const int idim) const
void	make_empty_vectors_and_weights (const long rank)
std::array< std::array< long, TENSOR_MAXDIM >, 2 >	make_vector_dimensions (const long rank) const
	deduce the dimensions of the left and right singular vectors from the tensor dimensions
Tensor< T >	make_vector_with_weights (const int dim) const
SRConf &	operator= (const SRConf &rhs)
	assignment operator (tested), shallow copy of vectors
SRConf &	operator= (const T &number)
	assign a number to this;
long	rank () const
	return the logicalrank
Tensor< T >	reconstruct () const
	reconstruct this to return a full tensor
Tensor< T > &	ref_vector (const unsigned int &idim)
	return reference to one of the vectors F
const Tensor< T > &	ref_vector (const unsigned int &idim) const
	return reference to one of the vectors F
template<typename Archive>
void	serialize (Archive &ar)
void	set_size_and_dim (long ndim, long k)
void	set_vectors_and_weights (const Tensor< typename Tensor< T >::scalar_type > &weights, const Tensor< T > &vector1, const Tensor< T > &vector2)
SRConf< T >	transform_dir (const Tensor< T > &c, const int &axis) const
double	weights (const unsigned int &i) const
	return the weight
Public Member Functions inherited from madness::BaseTensor
	BaseTensor ()
virtual	~BaseTensor ()
bool	conforms (const BaseTensor *t) const
	Returns true if this and *t are the same shape and size.
long	dim (int i) const
	Returns the size of dimension `i`.
const long *	dims () const
	Returns the array of tensor dimensions.
long	id () const
	Returns the typeid of the tensor (c.f., `TensorTypeData<T>` ).
bool	iscontiguous () const
	Returns true if the tensor refers to contiguous memory locations.
long	ndim () const
	Returns the number of dimensions in the tensor.
long	size () const
	Returns the number of elements in the tensor.
long	stride (int i) const
	Returns the stride associated with dimension `i`.
const long *	strides () const
	Returns the array of tensor strides.

Static Public Member Functions
static SVDTensor	compute_randomized_svd (const Tensor< T > &tensor, const double &eps, std::array< long, 2 > vectordim={0, 0})
	reduce the rank using SVD
static SVDTensor	compute_svd (const Tensor< T > &tensor, const double &eps, std::array< long, 2 > vectordim={0, 0})
	reduce the rank using SVD
static SVDTensor	compute_svd_from_range (const Tensor< T > &range, const Tensor< T > &matrix)
	compute an SVD from a given matrix and its range
static SVDTensor< T >	concatenate (const std::list< SVDTensor< T > > &addends)
	concatenate all arguments into a single SRConf (i.e. adding them all up)
static std::string	reduction_algorithm ()
static void	set_reduction_algorithm (const std::string alg)
Static Public Member Functions inherited from madness::SRConf< T >
static int	max_sigma (const double &thresh, const long &rank, const Tensor< double > &w)
Static Public Member Functions inherited from madness::BaseTensor
static int	get_instance_count ()
	Returns the count of all current instances of tensors & slice tensors of all types.

Friends
SVDTensor< T >	copy (const SVDTensor< T > &rhs)
template<typename R, typename Q>
SVDTensor< TENSOR_RESULT_TYPE(R, Q)>	general_transform (const SVDTensor< R > &t, const Tensor< Q > c[])
template<typename R, typename Q>
SVDTensor< TENSOR_RESULT_TYPE(R, Q)>	outer (const SVDTensor< R > &t1, const SVDTensor< Q > &t2)
SVDTensor< T >	reduce (std::list< SVDTensor< T > > &addends, double eps)
template<typename R, typename Q>
SVDTensor< TENSOR_RESULT_TYPE(R, Q)>	transform (const SVDTensor< R > &t, const Tensor< Q > &c)
template<typename R, typename Q>
SVDTensor< TENSOR_RESULT_TYPE(R, Q)>	transform_dir (const SVDTensor< R > &t, const Tensor< Q > &c, const int axis)

Additional Inherited Members
Public Types inherited from madness::SRConf< T >
typedef Tensor< T >	tensorT
Public Attributes inherited from madness::SRConf< T >
long	nci_left = -1
	separation dimensions: A(n,m) -> A(r,n) B(r,m), with n={k1,k2},m={k3,k4,k5..) multi-indices
std::vector< Slice >	s0
std::vector< Slice >	s1
std::array< Tensor< T >, 2 >	vector_
Tensor< typename Tensor< T >::scalar_type >	weights_
	for each configuration the weight; length should be r
Static Public Attributes inherited from madness::SRConf< T >
static const long	check_orthonormality_frequency = 100000
	check orthonormality at low rank additions at every xth addition
Protected Member Functions inherited from madness::SRConf< T >
bool	check_dimensions () const
bool	check_vector_orthonormality (const int ivec) const
	check if the terms are orthogonal
void	inplace_add (const SRConf< T > &rhs, std::array< Slice, TENSOR_MAXDIM > lhs_s, std::array< Slice, TENSOR_MAXDIM > rhs_s, const double alpha, const double beta)
	alpha * this(lhs_s) + beta * rhs(rhs_s)
bool	is_flat () const
	return if this has only one additional dimension (apart from rank)
Tensor< T >	make_left_vector_with_weights () const
void	make_slices ()
	redo the Slices for getting direct access to the configurations
std::array< std::vector< Slice >, 2 >	make_slices (const std::array< Slice, TENSOR_MAXDIM > &s) const
void	make_structure (bool force=false)
unsigned int	nCoeff () const
	return the number of coefficients
void	normalize ()
	normalize the vectors (tested)
TensorTypeData< T >::float_scalar_type	normf () const
	calculate the Frobenius norm
size_t	real_size () const
	return the real size of this
void	scale (const double &fac)
	scale this by a number
void	scale (const double_complex &fac)
TensorTypeData< T >::float_scalar_type	svd_normf () const
	calculate the Frobenius norm, if this is in SVD form
template<typename Q>
std::enable_if<!(TensorTypeData< T >::iscomplexorTensorTypeData< Q >::iscomplex), TENSOR_RESULT_TYPE(T, Q)>::type friend	trace (const SRConf< T > &rhs, const SRConf< Q > &lhs)
	calculate the Frobenius inner product (tested)
template<typename Q>
std::enable_if<(TensorTypeData< T >::iscomplexorTensorTypeData< Q >::iscomplex), TENSOR_RESULT_TYPE(T, Q)>::type friend	trace (const SRConf< T > &rhs, const SRConf< Q > &lhs)
SRConf< T >	transform (const Tensor< T > &c) const
Protected Member Functions inherited from madness::BaseTensor
void	cycledim_inplace (long shift, long start, long end)
	Cyclic shift of dimensions.
void	flat_inplace ()
	Reshapes the tensor inplace into 1D.
void	fusedim_inplace (long i)
	Fuses dimensions `i` and `i+1`.
void	mapdim_inplace (const std::vector< long > &map)
	General permutation of dimensions.
void	reshape_inplace (const std::vector< long > &d)
	Reshapes the tensor inplace.
void	reshape_inplace (int ndimnew, const long *d)
	Reshapes the tensor inplace.
void	set_dims_and_size (long nd, const long d[])
void	splitdim_inplace (long i, long dimi0, long dimi1)
	Splits dimension `i`.
void	swapdim_inplace (long i, long j)
	Swaps the dimensions.
Protected Attributes inherited from madness::BaseTensor
long	_dim [TENSOR_MAXDIM]
	Size of each dimension.
long	_id
	Id from TensorTypeData<T> in type_data.h.
long	_ndim
	Number of dimensions (-1=invalid; 0=no supported; >0=tensor).
long	_size
	Number of elements in the tensor.
long	_stride [TENSOR_MAXDIM]
	Increment between elements in each dimension.

Constructor & Destructor Documentation

◆ SVDTensor() [1/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor ( )

inline

References madness::SRConf< T >::SRConf().

Referenced by SVDTensor(), compute_randomized_svd(), compute_svd(), compute_svd_from_range(), concatenate(), copy, divide_and_conquer_reduce(), emul(), gaxpy(), general_transform, operator=(), outer, reduce, transform, and transform_dir.

◆ SVDTensor() [2/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor	(	const Tensor< T > &	rhs,
		const double	eps )

inline

References compute_randomized_svd(), and madness::Tensor< T >::has_data().

◆ SVDTensor() [3/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor ( const SVDTensor< T > & rhs )

default

References SVDTensor().

◆ SVDTensor() [4/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor ( const SRConf< T > & rhs )

inline

References madness::SRConf< T >::SRConf().

◆ SVDTensor() [5/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor ( const std::vector< long > & dims )

inline

References madness::SRConf< T >::SRConf(), madness::BaseTensor::dims(), madness::FunctionCommonData< T, NDIM >::data, and madness::BaseTensor::size().

◆ SVDTensor() [6/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor	(	const long	ndims,
		const long *	dims )

inline

References madness::SRConf< T >::SRConf(), and madness::BaseTensor::dims().

◆ SVDTensor() [7/7]

template<typename T>

madness::SVDTensor< T >::SVDTensor	(	const Tensor< double > &	weights,
		const Tensor< T > &	vector1,
		const Tensor< T > &	vector2,
		const long &	ndim,
		const long *	dims )

inline

References madness::SRConf< T >::SRConf(), madness::BaseTensor::dims(), madness::BaseTensor::ndim(), and madness::SRConf< T >::weights().

Member Function Documentation

◆ compute_randomized_svd()

template<typename T>

SVDTensor< T > madness::SVDTensor< T >::compute_randomized_svd	(	const Tensor< T > &	tensor,
		const double &	eps,
		std::array< long, 2 >	vectordim = {0,0} )