## Learn to Use ITensor

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# 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

//
//Singular value decomposition (SVD)
//
auto T = randomTensor(l1,l2,s1,s2);

//Providing indices of U tells the svd
//method which indices should end up on U,
//other indices of psi will go on V

ITensor U(l1,s1),S,V;
svd(T,U,S,V);

Print(norm(T-U*S*V)); //prints: 0.0

svd(T,U,S,V,{"Cutoff",1E-4});

Print(sqr(norm(T-U*S*V)/norm(T))); //prints: 1E-4

//
//Eigenvalue decomposition
//of Hermitian tensors
//
//Assumes matching pairs of indices
//with prime level 0 and 1
//

auto rho = ITensor(s1,s2,prime(s1),prime(s2));
//...set elements of rho...

ITensor U,D;
diagHermitian(rho,U,D);

Print(norm(rho-prime(U)*D*dag(U))); //prints: 0.0


## Singular Value Decomposition Algorithms

• svd(ITensor T, ITensor & U, ITensor & S, Tensor & V,
Args args = Args::global())

svd(IQTensor T, IQTensor & U, IQTensor & S, Tensor & V,
Args args = Args::global())


Compute 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.

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

The svd function also recognizes the following optional named arguments:

• "Maxm" — 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 $\epsilon$ :

$$\frac{\sum_{n\in\text{discarded}} \lambda^2_n}{\sum_{n} \lambda^2_n} < \epsilon \:.$$

• "Minm" — 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", "Maxm", "Minm") 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.

• "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.

• "LeftIndexName" — set the name of the index connecting S to U.

• "RightIndexName" — set the name of the index connecting S to V.

• "IndexType" — set the IndexType of the indices of S connecting to U and V.

• "LeftIndexType" — set just the IndexType of the index connecting S to U.

• "RightIndexType" — set just the IndexType of the index connecting S to V.

Click to Show Example
auto s1 = Index("Site 1",2,Site);
auto s2 = Index("Site 2",2,Site);
auto l1 = Index("Link 1",4,Link);
auto l2 = Index("Link 1",4,Link);

auto T = randomTensor(l1,s1,s2,l2);

//want l1, s1 to end up on U
auto U = ITensor(l1,s1);
//ok to leave S and V uninitialized
ITensor S,V;

//compute exact SVD
svd(T,U,D,V);

Print(norm(T-U*D*V)); //prints: 0.0

//compute approximate SVD
svd(T,U,D,V,{"Cutoff",1E-9});

Print(sqr(norm(T-U*D*V)/norm(T))); //prints: 1E-9

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

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


The "factor" decomposition is based on the SVD, but factorizes a tensor T into only two tensors T==A*B where A and B share a single common index.

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.

To decide which indices of T should go on the final A versus B, the code first inspects A for any common indices it shares with T; if found all of these indices go on A afterward with the rest on B. Otherwise B is used to determine how to split the indices.

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

Click to Show Example
auto T = ITensor(i,j,k,l);
//...set elements of T...

//putting i,k on A tells
//factor to keep these on A,
//put j,l on B
auto A = ITensor(i,k);
ITensor B; //uninitialized

factor(T,A,B);

Print(norm(T-A*B)); //prints: 0.0


## Hermitian Matrix Algorithms

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

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


Diagonalize a Hermitian tensor T such that T==dag(U)*D*prime(U). Tensors U and D are passed by reference and overwritten upon return.

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 == dag(swapPrime(T,0,1)).

Click to Show Example
auto i = Index("i",2);
auto j = Index("j",4);

T = randomTensor(i,j,prime(i),prime(j));

//Make Hermitian tensor out of T
auto H = T + swapPrime(T,0,1);

ITensor U,D;
diagHermitian(H,U,D);

Print((H-dag(U)*D*prime(U)).norm()); //prints: 0.0


The diagHermitian function recognizes the following optional named arguments:

• "Maxm" — 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 $\epsilon$ :

$$\frac{\sum_{n\in\text{discarded}} p_n}{\sum_{n} p_n} < \epsilon \:.$$

• "Minm" — integer m. At least m eigenvalues will be kept, even if the cutoff criterion would discard more.

• "IndexName" — string. Specify the name of the new index shared between U and D.

• "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","Maxm", "Minm") will be used to perform a truncation of singular values.

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

Given a Hermitian tensor H, with 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 Example
auto i = Index("i",2);
auto j = Index("j",4);

T = randomTensor(i,j,prime(i),prime(j));

//Make Hermitian tensor out of T
auto H = T + swapPrime(T,0,1);

// compute exp(i * H)
auto expiH = expHermitian(H,1_i);

// compute exp(2 * H)
auto exp2H = expHermitian(H,2);


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

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


Factorize a tensor T into products A and B such that T==A * B. A and B are passed by reference and overwritten to hold the results.

Returns: Spectrum object containing information about truncation and density matrix eigenvalues.

To determine which indices of T should end up on A versus B, the method inspects the initial indices of A (or B if A is default constructed) and keeps the same indices on A upon return, with the rest of the indices going onto B. (This is similar to how the svd method above works.)

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.)

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 "Maxm" described below.

The denmatDecomp function recognizes the following optional named arguments:

• "Maxm" — 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 $\epsilon$ :

$$\frac{\sum_{n\in\text{discarded}} \ p_n}{\sum_{n} p_n} < \epsilon \:.$$

• "Minm" — 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", "Maxm", "Minm") 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.

Click to Show Example
auto T = randomTensor(l1,s1,s2,l2);

auto A = ITensor(l1,s1); //want l1, s1 to end up on A
ITensor B;
denmatDecomp(T,A,B,Fromleft); //decompose T into A * B

Print(norm(T-A*B)); //prints: 0


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

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


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.

This page current as of version 2.0.10

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