## Learn to Use ITensor

# Tensor Decompositions

Methods for computing decompositions such as the singular value decomposition (SVD), Hermitian diagonalization, and density matrix diagonalization.

These methods are defined in "itensor/decomp.h" and "itensor/decomp.cc".

## Synopsis

```
auto l1 = Index(12,"l1");
auto l2 = Index(12,"l2");
auto s1 = Index(4,"s1");
auto s2 = Index(4,"s2");
//
//Singular value decomposition (SVD)
//
auto T = randomITensorC(l1,l2,s1,s2);
//The IndexSet specifies which indices
//of T will end up on U.
//The output is a tuple of the ITensors
//of the decomposition and the new
//indices created in the decomposition
auto [U,S,V] = svd(T,{l1,s1});
Print(norm(T-U*S*V)); //On the order of 1E-15
std::tie(U,S,V) = svd(T,{l1,s1},{"Cutoff",1E-4});
Print(sqr(norm(T-U*S*V)/norm(T))); //On the order of 1E-4
//
//Eigenvalue decomposition
//of Hermitian tensors
//
//Assumes matching pairs of indices
//with prime level 0 and 1
//
auto H = randomITensorC(s1,s2,prime(s1),prime(s2));
H = 0.5*(H+dag(swapTags(H,"0","1")));
auto [Q,D] = diagHermitian(H);
Print(norm(H-prime(Q)*D*dag(Q))); //On the order of 1E-15
```

## Singular Value Decomposition

svd(ITensor T, IndexSet Uis[, IndexSet Vis], Args args = Args::global()) -> std::tuple<ITensor,ITensor,ITensor>

Compute the singular value decomposition of an ITensor

`T`

. A tuple of the`U`

,`S`

and`V`

tensors of the decomposition. The product`U*S*V`

is equal to T (up to truncation errors).To get the newly introduced indices, you can use and

`auto u = commonIndex(U,S)`

and`auto v= commonIndex(S,V)`

.An IndexSet

`Uis`

is input along with`T`

to specify which Indices of`T`

end up on the`U`

tensor of the decomposition, and the rest of the indices end up on the`V`

tensor.Optionally, the indices ending up on the output tensor

`V`

can be explicitly specified by the IndexSet`Vis`

. In this case, an extra check is added that the union of the`Uis`

indices and`Vis`

indices are the same as the indices on`T`

.The svd function also recognizes the following optional named arguments:

"MaxDim" — integer M. If there are more than M singular values, only the largest M are kept.

"Cutoff" — real number @@\epsilon@@ . Discard the smallest singular values @@\lambda_n@@ such that the

*truncation error*is less than @@\epsilon@@ :$$ \frac{\sum_{n\in\text{discarded}} \lambda^2_n}{\sum_{n} \lambda^2_n} < \epsilon \:. $$"MinDim" — integer M. At least M singular values will be kept, even if the cutoff criterion would discard more.

"Truncate" — if set to

`false`

, no truncation occurs. Otherwise truncation parameters ("Cutoff", "MaxDim", "MinDim") will be used to perform a truncation of singular values."ShowEigs" — if

`true`

, print lots of extra information about the truncation of singular values. Default is`false`

."SVDMethod" — string, equal to either "ITensor", "gesdd", or "gesvd". The "SVDMethod" argument controls which implementation of the SVD is used. "ITensor" is the most reliable and is therefore the default. The "gesdd" algorithm is typically much faster and more accurate than the "ITensor" algorithm, but occasionally has been observed to crash depending on the LAPACK implementation used. The "gesvd" is an alternative LAPACK algorithm.

"SVDThreshold" — real number less than 1.0; default is 1e-3. If the ratio of any singular values to the largest value fall below this number, the SVD algorithm will be recursively applied to the part of the matrix containing these small values to achieve better accuracy. Setting this number larger can make the SVD more accurate if the singular values decrease very rapidly.

"LeftTags" — set just the tags of the index connecting S to U.

"RightTags" — set just the tags of the index connecting S to V.

"RespectDegenerate" — if

`true`

, degenerate subspaces will be respected (i.e., if the the truncation lies within a set of degenerate singular values based on a numerical threshold, the degenerate subspace will either be entirely truncated or kept, depending on the other argument options). Default is`false`

.

Click to Show Exampleauto s1 = Index(4,"Site"); auto s2 = Index(4,"Site"); auto l1 = Index(12,"Link"); auto l2 = Index(12,"Link"); auto T = randomITensor(l1,s1,s2,l2); //Compute SVD without truncation //Specify we want l1, s1 to end up on U auto [U,S,V] = svd(T,{l1,s1}); Print(norm(T-U*S*V)); //prints on order of 1E-15 //compute approximate SVD std::tie(U,S,V) = svd(T,{l1,s1},{"Cutoff",1E-4}); Print(sqr(norm(T-U*S*V)/norm(T))); //prints on order of 1E-4

## Hermitian Diagonalization

diagHermitian(ITensor H, Args args = Args::global()) -> std::tuple<ITensor,ITensor>

Diagonalize an ITensor T the can be interpreted as a Hermitian matrix under appropriate grouping of indices. Results in an ITensor

`U`

of orthonormal eigenvectors and diagonal ITensor`D`

of real eigenvalues such that`T==dag(U)*D*prime(U)`

.To get the newly introduced index, you can use

`auto u = commonIndex(U,D)`

.`D`

contains indices`u`

and`prime(u)`

.The method assumes that the indices of T come in pairs, one index with prime level 0 and a the same index but with prime level 1 (reflecting the Hermitian nature of T). For example, T could have indices {i,i',j,j'}. Saying that T is Hermitian means that

`T == swapTags(dag(T),"0","1")`

.The

`diagHermitian`

function recognizes the following optional named arguments:- "Tags" — TagSet. Specify the tags of the index shared by U and D.

Click to Show Exampleauto i = Index(2); auto j = Index(4); auto T = randomITensorC(i,j,prime(i),prime(j)); //Make Hermitian tensor out of T auto H = T + swapTags(dag(T),"0","1"); auto [U,D] = diagHermitian(H); auto u = commonIndex(U,D); Print(hasInds(D,{u,prime(u)})); //prints: true Print(norm(H-dag(U)*D*prime(U))); //prints on the order of 1E-15

diagPosSemiDef(ITensor H, Args args = Args::global()) -> std::tuple<ITensor,ITensor>

Same as

`diagHermitian`

above, but allows truncation according to the eigenvalues. Truncation is performed assuming the ITensor, reinterpreted as a matrix, is approximately positive semi-definite (i.e. the eigenvalues are approximately non-negative). If truncation is performed, negative eigenvalues will be set to zero.Along with the named arguments of

`diagHermitian`

,`diagPosSemiDef`

accepts the following arguments:"MaxDim" — integer M. If there are more than M eigenvalues, only the largest M are kept.

"Cutoff" — real number @@\epsilon@@ . Discard the smallest eigenvalues @@p_n@@ such that the

*truncation error*is less than @@\epsilon@@ :$$ \frac{\sum_{n\in\text{discarded}} p_n}{\sum_{n} p_n} < \epsilon \:. $$"MinDim" — integer m. At least m eigenvalues will be kept, even if the cutoff criterion would discard more.

"ShowEigs" — if

`true`

, print lots of extra information about the truncation of singular values."Truncate" — if set to

`false`

, no truncation occurs. Otherwise truncation parameters ("Cutoff","MaxDim", "MinDim") will be used to perform a truncation of singular values.

"RespectDegenerate" — if

`true`

, degenerate subspaces will be respected (i.e., if the the truncation lies within a set of degenerate singular values based on a numerical threshold, the degenerate subspace will either be entirely truncated or kept, depending on the other argument options). Default is`false`

.

## General Factorizations

factor(ITensor T, IndexSet Ais[, IndexSet Bis], Args args = Args::global()) -> std::tuple<ITensor,ITensor>

The "factor" decomposition is based on the SVD, but factorizes a tensor T into only two tensors

`T==A*B`

(up to truncation errors) where A and B share a single common index. The function returns`A`

and`B`

as ITensors. The newly introduced index can be obtained with`auto l = commonIndex(A,B)`

.If the SVD of T is

`T==U*S*V`

where S is a diagonal matrix of singular values, then A and B are schematically`A==U*sqrt(S)`

and`B==sqrt(S)*V`

.An IndexSet

`Ais`

is input along with`T`

to specify which Indices of`T`

end up on the`A`

tensor of the decomposition, and the rest of the indices end up on the`B`

tensor.Optionally, the indices ending up on the output tensor

`B`

can be explicitly specified by the IndexSet`Bis`

. In this case, an extra check is added that the union of the`Ais`

indices and`Bis`

indices are the same as the indices on`T`

.In addition to the named Args recognized by the svd routine, factor accepts an Arg "Tags" which will be the name of the common index connecting A and B.

Click to Show Exampleauto T = randomITensor(i,j,k,l); //Put i,k on A auto [A,B] = factor(T,{i,k}); Print(norm(T-A*B)); //prints on the order of 1E-15

denmatDecomp(ITensor T, IndexSet Ais[, IndexSet Bis], Direction dir, Args args = Args::global()) -> std::tuple<ITensor,ITensor>

Factorize a tensor T into products A and B such that

`T==A*B`

(up to truncation errors).If

`dir==Fromleft`

the tensor A will be "left orthogonal" in the sense that A times the conjugate of A summed over all indices not in common with B will produce an identity (Kronecker delta) tensor. If`dir==Fromright`

B will be unitary ("right orthogonal").If

`dir==Fromleft`

, the result of this method is equivalent to computing an SVD of T such that`T==U*D*V`

then setting`A=U`

and`B=D*V`

. (If`dir==Fromright`

it would be equivalent to setting`A=U*D`

and`B=V`

.)An IndexSet

`Ais`

is input along with`T`

to specify which Indices of`T`

end up on the`A`

tensor of the decomposition, and the rest of the indices end up on the`B`

tensor.Optionally, the indices ending up on the output tensor

`B`

can be explicitly specified by the IndexSet`Bis`

. In this case, an extra check is added that the union of the`Ais`

indices and`Bis`

indices are the same as the indices on`T`

.Although the results of this method are related to the SVD, the implementation is different. Rather than performing an SVD, the method computes a "density matrix" from T (using an analogy where T is a wave function, which may or may not actually be the case) and diagonalizes this density matrix. Two key reasons for doing this versus an SVD are computational efficiency and having the ability to implement the DMRG noise term (see the next version of

`denmatDecomp`

below).To compute a truncated version of this decomposition, pass one or both of the named arguments "Cutoff" or "MaxDim" described below.

The denmatDecomp function recognizes the following optional named arguments:

- "MaxDim" — integer M. If there are more than M eigenvalues, only the largest M are kept.

"Cutoff" — real number @@\epsilon@@ . Discard the smallest eigenvalues @@p_n@@ such that the

*truncation error*is less than @@\epsilon@@ :$$ \frac{\sum_{n\in\text{discarded}} \ p_n}{\sum_{n} p_n} < \epsilon \:. $$"MinDim" — integer M. At least M singular values will be kept, even if they fall below the cutoff.

"Truncate" — if set to

`false`

, no truncation occurs. Otherwise truncation parameters ("Cutoff", "MaxDim", "MinDim") will be used to perform a truncation of singular values."ShowEigs" — if

`true`

, print lots of extra information about the truncation of singular values. Default is`false`

."RespectDegenerate" — if

`true`

, degenerate subspaces will be respected (i.e., if the the truncation lies within a set of degenerate singular values based on a numerical threshold, the degenerate subspace will either be entirely truncated or kept, depending on the other argument options). Default is`false`

.

Click to Show Exampleauto T = randomITensor(l1,s1,s2,l2); auto [A,B,l] = denmatDecomp(T,{l1,s1},Fromleft); //decompose T into A * B Print(l == commonIndex(A,B)); //prints: true Print(norm(T-A*B)); //prints on the order of 1E-15

template<class BigMatrixT> denmatDecomp(ITensor T, IndexSet Ais[, IndexSet Bis], Direction dir, BigMatrixT PH, Args args = Args::global()) -> std::tuple<ITensor,ITensor>

Identical to denmatDecomp function described above, except before the decomposition the density matrix formed from T has the "noise term" added to it. For more information on the noise term see the paper Density matrix renormalization group algorithms with a single center site, S.R. White,

*Phys. Rev. B**72*, 180403(R) (2005).For the PH object to implement the noise term, it must provide the

`deltaRho`

method. For more information see the documentation on LocalOp.Named arguments recognized:

- "Noise" — real number. Coefficient of noise term; default is 0.

## Hermitian Matrix Exponentiation

`expHermitian(ITensor H, Cplx tau = 1) -> ITensor`

Given an ITensor H that can be treated as a Hermitian matrix (by matching pairs of indices, one with prime level zero, the other with prime level 1), returns the exponential of this tensor.

Optionally a factor

`tau`

can be included in the exponent. If`tau`

has zero imaginary part and the tensor H is real, the returned tensor will also be real.Click to Show Exampleauto i = Index(2); auto j = Index(4); auto T = randomITensor(i,j,prime(i),prime(j)); //Make Hermitian matrix out of T auto H = T + swapTags(dag(T),"0","1"); // compute exp(i * H) auto expiH = expHermitian(H,1_i); // compute exp(2 * H) auto exp2H = expHermitian(H,2);

## Old Syntax and Spectrum Output

The following are older interfaces for ITensor decomposition functions for backwards compatibility and internal usage.

svd(ITensor T, ITensor & U, ITensor & S, ITensor & V, Args args = Args::global()) -> Spectrum

diagHermitian(ITensor H, ITensor & U, ITensor & D, Args args = Args::global()) -> Spectrum

diagPosSemiDef(ITensor H, ITensor & U, ITensor & D, Args args = Args::global()) -> Spectrum

factor(ITensor T, ITensor & A, ITensor & B, Args args = Args::global()) -> Spectrum

denmatDecomp(ITensor T, ITensor & A, ITensor & B, Direction dir, Args args = Args::global()) -> Spectrum

template<class BigMatrixT> denmatDecomp(ITensor T, ITensor & A, ITensor & B, Direction dir, BigMatrixT PH, Args args = Args::global()) -> Spectrum

These are alternative interfaces for the decompositions listed above. For example,

`svd(T,U,S,V,args)`

computes the singular value decomposition of a tensor T. The arguments U, S, and V are overwritten, and the product`U*S*V`

equals T.*Returns*: Spectrum object containing information about truncation and singular values/eigenspectrum.For

`svd(T,U,S,V)`

,`factor(T,A,B)`

, and`denmatDecomp(T,A,B,dir[,PH])`

, to determine which indices should be grouped together as the "row" indices and thus end up on the final U (A) tensor, versus the remaining "column" indices which end up on the final V (B) tensor, the function inspects the indices of the input U (A) tensor. All indices common to both U (A) and T are considered "row" indices (other indices of U (A) are ignored). If U (A) has no indices, the function inspects the indices of V (B).

*This page current as of version 3.0.0*

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