ITensor

Description

ITensors.ITensorType
ITensor

An ITensor is a tensor whose interface is independent of its memory layout. Therefore it is not necessary to know the ordering of an ITensor's indices, only which indices an ITensor has. Operations like contraction and addition of ITensors automatically handle any memory permutations.

Examples

julia> i = Index(2, "i")
(dim=2|id=287|"i")

#
# Make an ITensor with random elements:
#
julia> A = randomITensor(i', i)
ITensor ord=2 (dim=2|id=287|"i")' (dim=2|id=287|"i")
NDTensors.Dense{Float64,Array{Float64,1}}

julia> @show A;
A = ITensor ord=2
Dim 1: (dim=2|id=287|"i")'
Dim 2: (dim=2|id=287|"i")
NDTensors.Dense{Float64,Array{Float64,1}}
2×2
0.28358594718392427   1.4342219756446355
1.6620103556283987   -0.40952231269251566

julia> @show inds(A);
inds(A) = ((dim=2|id=287|"i")', (dim=2|id=287|"i"))

#
# Set the i==1, i'==2 element to 1.0:
#
julia> A[i => 1, i' => 2] = 1;

julia> @show A;
A = ITensor ord=2
Dim 1: (dim=2|id=287|"i")'
Dim 2: (dim=2|id=287|"i")
NDTensors.Dense{Float64,Array{Float64,1}}
2×2
0.28358594718392427   1.4342219756446355
1.0                  -0.40952231269251566

julia> @show storage(A);
storage(A) = [0.28358594718392427, 1.0, 1.4342219756446355, -0.40952231269251566]

julia> B = randomITensor(i, i');

julia> @show B;
B = ITensor ord=2
Dim 1: (dim=2|id=287|"i")
Dim 2: (dim=2|id=287|"i")'
NDTensors.Dense{Float64,Array{Float64,1}}
2×2
-0.6510816500352691   0.2579101497658179
0.256266641521826   -0.9464735926768166

#
# Can add or subtract ITensors as long as they
# have the same indices, in any order:
#
julia> @show A + B;
A + B = ITensor ord=2
Dim 1: (dim=2|id=287|"i")'
Dim 2: (dim=2|id=287|"i")
NDTensors.Dense{Float64,Array{Float64,1}}
2×2
-0.3674957028513448   1.6904886171664615
1.2579101497658178  -1.3559959053693322
source

Dense Constructors

ITensors.ITensorMethod
ITensor([::Type{ElT} = Float64, ]inds)
ITensor([::Type{ElT} = Float64, ]inds::Index...)

Construct an ITensor filled with zeros having indices inds and element type ElT. If the element type is not specified, it defaults to Float64.

The storage will have NDTensors.Dense type.

Examples

i = Index(2,"index_i")
j = Index(4,"index_j")
k = Index(3,"index_k")

A = ITensor(i,j)
B = ITensor(ComplexF64,k,j)
source
ITensors.ITensorMethod
ITensor([::Type{ElT} = Float64, ]::UndefInitializer, inds)
ITensor([::Type{ElT} = Float64, ]::UndefInitializer, inds::Index...)

Construct an ITensor filled with undefined elements having indices inds and element type ElT. If the element type is not specified, it defaults to Float64. One purpose for using this constructor is that initializing the elements in an undefined way is faster than initializing them to a set value such as zero.

The storage will have NDTensors.Dense type.

Examples

i = Index(2,"index_i")
j = Index(4,"index_j")
k = Index(3,"index_k")

A = ITensor(undef,i,j)
B = ITensor(ComplexF64,undef,k,j)
source
ITensors.ITensorMethod
ITensor([ElT::Type, ]x::Number, inds)
ITensor([ElT::Type, ]x::Number, inds::Index...)

Construct an ITensor with all elements set to x and indices inds.

If x isa Int or x isa Complex{Int} then the elements will be set to float(x) unless specified otherwise by the first input.

The storage will have NDTensors.Dense type.

Examples

i = Index(2,"index_i"); j = Index(4,"index_j"); k = Index(3,"index_k");

A = ITensor(1.0, i, j)
A = ITensor(1, i, j) # same as above
B = ITensor(2.0+3.0im, j, k)
Warning

In future versions this may not automatically convert integer inputs with float, and in that case the particular element type should not be relied on.

source
ITensors.ITensorMethod
ITensor([ElT::Type, ]A::Array, inds)
ITensor([ElT::Type, ]A::Array, inds::Index...)

itensor([ElT::Type, ]A::Array, inds)
itensor([ElT::Type, ]A::Array, inds::Index...)

Construct an ITensor from an Array A and indices inds. The ITensor will be a view of the Array data if possible (if no conversion to a different element type is necessary).

If specified, the ITensor will have element type ElT.

If the element type of A is Int or Complex{Int} and the desired element type isn't specified, it will be converted to Float64 or Complex{Float64} automatically. To keep the element type as an integer, specify it explicitly, for example with:

i = Index(2, "i")
A = [0 1; 1 0]
T = ITensor(eltype(A), A, i', dag(i))

Examples

i = Index(2,"index_i")
j = Index(2,"index_j")

M = [1. 2;
3 4]
T = ITensor(M, i, j)
T[i => 1, j => 1] = 3.3
M[1, 1] == 3.3
T[i => 1, j => 1] == 3.3
Warning

In future versions this may not automatically convert Int/Complex{Int} inputs to floating point versions with float (once tensor operations using Int/Complex{Int} are natively as fast as floating point operations), and in that case the particular element type should not be relied on. To avoid extra conversions (and therefore allocations) it is best practice to directly construct with itensor([0. 1; 1 0], i', dag(i)) if you want a floating point element type. The conversion is done as a performance optimization since often tensors are passed to BLAS/LAPACK and need to be converted to floating point types compatible with those libraries, but future projects in Julia may allow for efficient operations with more general element types (for example see https://github.com/JuliaLinearAlgebra/Octavian.jl).

source
ITensors.randomITensorMethod
randomITensor([::Type{ElT <: Number} = Float64, ]inds)
randomITensor([::Type{ElT <: Number} = Float64, ]inds::Index...)

Construct an ITensor with type ElT and indices inds, whose elements are normally distributed random numbers. If the element type is not specified, it defaults to Float64.

Examples

i = Index(2,"index_i")
j = Index(4,"index_j")
k = Index(3,"index_k")

A = randomITensor(i,j)
B = randomITensor(ComplexF64,undef,k,j)
source
ITensors.onehotFunction
onehot(ivs...)
setelt(ivs...)
onehot(::Type, ivs...)
setelt(::Type, ivs...)

Create an ITensor with all zeros except the specified value, which is set to 1.

Examples

i = Index(2,"i")
A = onehot(i=>2)
# A[i=>2] == 1, all other elements zero

# Specify the element type
A = onehot(Float32, i=>2)

j = Index(3,"j")
B = onehot(i=>1,j=>3)
# B[i=>1,j=>3] == 1, all other element zero
source

Dense View Constructors

ITensors.itensorMethod
itensor(args...; kwargs...)

Like the ITensor constructor, but with attempt to make a view of the input data when possible.

source

QN BlockSparse Constructors

ITensors.ITensorMethod
ITensor([::Type{ElT} = Float64, ][flux::QN = QN(), ]inds)
ITensor([::Type{ElT} = Float64, ][flux::QN = QN(), ]inds::Index...)

Construct an ITensor with BlockSparse storage filled with zero(ElT) where the nonzero blocks are determined by flux.

If ElT is not specified it defaults to Float64.

If flux is not specified, the ITensor will be empty (it will contain no blocks, and have an undefined flux). The flux will be set by the first element that is set.

Examples

julia> i
(dim=3|id=212|"i") <Out>
1: QN(0) => 1
2: QN(1) => 2

julia> @show ITensor(QN(0), i', dag(i));
ITensor(QN(0), i', dag(i)) = ITensor ord=2
Dim 1: (dim=3|id=212|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=212|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.BlockSparse{Float64, Vector{Float64}, 2}
3×3
Block(1, 1)
[1:1, 1:1]
0.0

Block(2, 2)
[2:3, 2:3]
0.0  0.0
0.0  0.0

julia> @show ITensor(QN(1), i', dag(i));
ITensor(QN(1), i', dag(i)) = ITensor ord=2
Dim 1: (dim=3|id=212|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=212|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.BlockSparse{Float64, Vector{Float64}, 2}
3×3
Block(2, 1)
[2:3, 1:1]
0.0
0.0

julia> @show ITensor(ComplexF64, QN(1), i', dag(i));
ITensor(ComplexF64, QN(1), i', dag(i)) = ITensor ord=2
Dim 1: (dim=3|id=212|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=212|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.BlockSparse{ComplexF64, Vector{ComplexF64}, 2}
3×3
Block(2, 1)
[2:3, 1:1]
0.0 + 0.0im
0.0 + 0.0im

julia> @show ITensor(undef, QN(1), i', dag(i));
ITensor(undef, QN(1), i', dag(i)) = ITensor ord=2
Dim 1: (dim=3|id=212|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=212|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.BlockSparse{Float64, Vector{Float64}, 2}
3×3
Block(2, 1)
[2:3, 1:1]
0.0
1.63e-322

Construction with undefined flux:

julia> A = ITensor(i', dag(i));

julia> @show A;
A = ITensor ord=2
Dim 1: (dim=3|id=212|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=212|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.EmptyStorage{NDTensors.EmptyNumber, NDTensors.BlockSparse{NDTensors.EmptyNumber, Vector{NDTensors.EmptyNumber}, 2}}
3×3

julia> isnothing(flux(A))
true

julia> A[i' => 1, i => 2] = 2
2

julia> @show A;
A = ITensor ord=2
Dim 1: (dim=3|id=212|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=212|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.BlockSparse{Int64, Vector{Int64}, 2}
3×3
Block(1, 2)
[1:1, 2:3]
2  0

julia> flux(A)
QN(-1)
source
ITensors.ITensorMethod
ITensor([ElT::Type, ]::AbstractArray, inds; tol = 0)

Create a block sparse ITensor from the input Array, and collection of QN indices. Zeros are dropped and nonzero blocks are determined from the zero values of the array.

Optionally, you can set a tolerance such that elements less than or equal to the tolerance are dropped.

Examples

julia> i = Index([QN(0)=>1, QN(1)=>2], "i");

julia> A = [1e-9 0.0 0.0;
0.0 2.0 3.0;
0.0 1e-10 4.0];

julia> @show ITensor(A, i', dag(i); tol = 1e-8);
ITensor(A, i', dag(i); tol = 1.0e-8) = ITensor ord=2
Dim 1: (dim=3|id=468|"i")' <Out>
1: QN(0) => 1
2: QN(1) => 2
Dim 2: (dim=3|id=468|"i") <In>
1: QN(0) => 1
2: QN(1) => 2
NDTensors.BlockSparse{Float64,Array{Float64,1},2}
3×3
Block: (2, 2)
[2:3, 2:3]
2.0  3.0
0.0  4.0
source
ITensors.ITensorMethod
ITensor([::Type{ElT} = Float64,] ::UndefInitializer, flux::QN, inds)
ITensor([::Type{ElT} = Float64,] ::UndefInitializer, flux::QN, inds::Index...)

Construct an ITensor with indices inds and BlockSparse storage with undefined elements of type ElT, where the nonzero (allocated) blocks are determined by the provided QN flux. One purpose for using this constructor is that initializing the elements in an undefined way is faster than initializing them to a set value such as zero.

The storage will have NDTensors.BlockSparse type.

Examples

i = Index([QN(0)=>1, QN(1)=>2], "i")
A = ITensor(undef,QN(0),i',dag(i))
B = ITensor(Float64,undef,QN(0),i',dag(i))
C = ITensor(ComplexF64,undef,QN(0),i',dag(i))
source

Diagonal constructors

ITensors.diagITensorMethod
diagITensor([::Type{ElT} = Float64, ]inds)
diagITensor([::Type{ElT} = Float64, ]inds::Index...)

Make a sparse ITensor of element type ElT with only elements along the diagonal stored. Defaults to having zero(T) along the diagonal.

The storage will have NDTensors.Diag type.

source
ITensors.diagITensorMethod
diagITensor([ElT::Type, ]v::Vector, inds...)
diagitensor([ElT::Type, ]v::Vector, inds...)

Make a sparse ITensor with non-zero elements only along the diagonal. In general, the diagonal elements will be those stored in v and the ITensor will have element type eltype(v), unless specified explicitly by ElT. The storage will have NDTensors.Diag type.

In the case when eltype(v) isa Union{Int, Complex{Int}}, by default it will be converted to float(v). Note that this behavior is subject to change in the future.

The version diagITensor will never output an ITensor whose storage data is an alias of the input vector data.

The version diagitensor might output an ITensor whose storage data is an alias of the input vector data in order to minimize operations.

source
ITensors.diagITensorMethod
diagITensor([ElT::Type, ]x::Number, inds...)
diagitensor([ElT::Type, ]x::Number, inds...)

Make a sparse ITensor with non-zero elements only along the diagonal. In general, the diagonal elements will be set to the value x and the ITensor will have element type eltype(x), unless specified explicitly by ElT. The storage will have NDTensors.Diag type.

In the case when x isa Union{Int, Complex{Int}}, by default it will be converted to float(x). Note that this behavior is subject to change in the future.

source
ITensors.deltaMethod
delta([::Type{ElT} = Float64, ]inds)
delta([::Type{ElT} = Float64, ]inds::Index...)

Make a uniform diagonal ITensor with all diagonal elements one(ElT). Only a single diagonal element is stored.

This function has an alias δ.

source

QN Diagonal constructors

ITensors.diagITensorMethod
diagITensor([::Type{ElT} = Float64, ][flux::QN = QN(), ]is)
diagITensor([::Type{ElT} = Float64, ][flux::QN = QN(), ]is::Index...)

Make an ITensor with storage type NDTensors.DiagBlockSparse with elements zero(ElT). The ITensor only has diagonal blocks consistent with the specified flux.

If the element type is not specified, it defaults to Float64. If theflux is not specified, it defaults to QN().

source
ITensors.deltaMethod
delta([::Type{ElT} = Float64, ][flux::QN = QN(), ]is)
delta([::Type{ElT} = Float64, ][flux::QN = QN(), ]is::Index...)

Make an ITensor with storage type NDTensors.DiagBlockSparse with uniform elements one(ElT). The ITensor only has diagonal blocks consistent with the specified flux.

If the element type is not specified, it defaults to Float64. If theflux is not specified, it defaults to QN().

source

Convert to Array

Core.ArrayMethod
Array{ElT, N}(T::ITensor, i:Index...)
Array{ElT}(T::ITensor, i:Index...)
Array(T::ITensor, i:Index...)

Matrix{ElT}(T::ITensor, row_i:Index, col_i::Index)
Matrix(T::ITensor, row_i:Index, col_i::Index)

Vector{ElT}(T::ITensor)
Vector(T::ITensor)

Given an ITensor T with indices i..., returns an Array with a copy of the ITensor's elements. The order in which the indices are provided indicates the order of the data in the resulting Array.

source
NDTensors.arrayMethod
array(T::ITensor, inds...)

Convert an ITensor T to an Array.

The ordering of the elements in the Array are specified by the input indices inds. This tries to avoid copying of possible (i.e. may return a view of the original data), for example if the ITensor's storage is Dense and the indices are already in the specified ordering so that no permutation is required.

Warning

Note that in the future we may return specialized AbstractArray types for certain storage types, for example a LinearAlgebra.Diagonal type for an ITensor with Diag storage. The specific storage type shouldn't be relied upon.

See also matrix, vector.

source
NDTensors.matrixMethod
matrix(T::ITensor, inds...)

Convert an ITensor T to a Matrix.

The ordering of the elements in the Matrix are specified by the input indices inds. This tries to avoid copying of possible (i.e. may return a view of the original data), for example if the ITensor's storage is Dense and the indices are already in the specified ordering so that no permutation is required.

Warning

Note that in the future we may return specialized AbstractArray types for certain storage types, for example a LinearAlgebra.Diagonal type for an ITensor with Diag storage. The specific storage type shouldn't be relied upon.

See also array, vector.

source
NDTensors.vectorMethod
vector(T::ITensor, inds...)

Convert an ITensor T to an Vector.

The ordering of the elements in the Array are specified by the input indices inds. This tries to avoid copying of possible (i.e. may return a view of the original data), for example if the ITensor's storage is Dense and the indices are already in the specified ordering so that no permutation is required.

Warning

Note that in the future we may return specialized AbstractArray types for certain storage types, for example a LinearAlgebra.Diagonal type for an ITensor with Diag storage. The specific storage type shouldn't be relied upon.

See also array, matrix.

source
NDTensors.arrayMethod
array(T::ITensor)

Given an ITensor T, returns an Array with a copy of the ITensor's elements, or a view in the case the the ITensor's storage is Dense.

The ordering of the elements in the Array, in terms of which Index is treated as the row versus column, depends on the internal layout of the ITensor.

Warning

This method is intended for developer use only and not recommended for use in ITensor applications unless you know what you are doing (for example you are certain of the memory ordering of the ITensor because you permuted the indices into a certain order).

See also matrix, vector.

source
NDTensors.matrixMethod
matrix(T::ITensor)

Given an ITensor T with two indices, returns a Matrix with a copy of the ITensor's elements, or a view in the case the ITensor's storage is Dense.

The ordering of the elements in the Matrix, in terms of which Index is treated as the row versus column, depends on the internal layout of the ITensor.

Warning

This method is intended for developer use only and not recommended for use in ITensor applications unless you know what you are doing (for example you are certain of the memory ordering of the ITensor because you permuted the indices into a certain order).

See also array, vector.

source

Getting and setting elements

Base.getindexMethod
getindex(T::ITensor, ivs...)

Get the specified element of the ITensor, using a list of IndexVals or Pair{<:Index, Int}.

Example

i = Index(2; tags = "i")
A = ITensor(2.0, i, i')
A[i => 1, i' => 2] # 2.0, same as: A[i' => 2, i => 1]
source
Base.setindex!Method
setindex!(T::ITensor, x::Number, ivs...)

setindex!(T::ITensor, x::Number, I::Integer...)

setindex!(T::ITensor, x::Number, I::CartesianIndex)

Set the specified element of the ITensor, using a list of Pair{<:Index, Integer} (or IndexVal).

If just integers are used, set the specified element of the ITensor using internal Index ordering of the ITensor (only for advanced usage, only use if you know the axact ordering of the indices).

Example

i = Index(2; tags = "i")
A = ITensor(i, i')
A[i => 1, i' => 2] = 1.0 # same as: A[i' => 2, i => 1] = 1.0
A[1, 2] = 1.0 # same as: A[i => 1, i' => 2] = 1.0

# Some simple slicing is also supported
A[i => 2, i' => :] = [2.0 3.0]
A[2, :] = [2.0 3.0]
source

Properties

ITensors.dirMethod
dir(A::ITensor, i::Index)

Return the direction of the Index i in the ITensor A.

source

Priming and tagging

ITensors.primeMethod
prime[!](A::ITensor, plinc::Int = 1; <keyword arguments>) -> ITensor

prime(inds, plinc::Int = 1; <keyword arguments>) -> IndexSet

Increase the prime level of the indices of an ITensor or collection of indices.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

source
ITensors.setprimeMethod
setprime[!](A::ITensor, plev::Int; <keyword arguments>) -> ITensor

setprime(inds, plev::Int; <keyword arguments>) -> IndexSet

Set the prime level of the indices of an ITensor or collection of indices.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

source
ITensors.noprimeMethod
noprime[!](A::ITensor; <keyword arguments>) -> ITensor

noprime(inds; <keyword arguments>) -> IndexSet

Set the prime level of the indices of an ITensor or collection of indices to zero.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

source
ITensors.mapprimeMethod
replaceprime[!](A::ITensor, plold::Int, plnew::Int; <keyword arguments>) -> ITensor
replaceprime[!](A::ITensor, plold => plnew; <keyword arguments>) -> ITensor
mapprime[!](A::ITensor, <arguments>; <keyword arguments>) -> ITensor

replaceprime(inds, plold::Int, plnew::Int; <keyword arguments>)
replaceprime(inds::IndexSet, plold => plnew; <keyword arguments>)
mapprime(inds, <arguments>; <keyword arguments>)

Set the prime level of the indices of an ITensor or collection of indices with prime level plold to plnew.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

source
ITensors.swapprimeMethod
swapprime[!](A::ITensor, pl1::Int, pl2::Int; <keyword arguments>) -> ITensor
swapprime[!](A::ITensor, pl1 => pl2; <keyword arguments>) -> ITensor

swapprime(inds, pl1::Int, pl2::Int; <keyword arguments>)
swapprime(inds, pl1 => pl2; <keyword arguments>)

Set the prime level of the indices of an ITensor or collection of indices with prime level pl1 to pl2, and those with prime level pl2 to pl1.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

source
ITensors.addtagsMethod
addtags[!](A::ITensor, ts::String; <keyword arguments>) -> ITensor

addtags(inds, ts::String; <keyword arguments>)

Add the tags ts to the indices of an ITensor or collection of indices.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

source
ITensors.removetagsMethod
removetags[!](A::ITensor, ts::String; <keyword arguments>) -> ITensor

removetags(inds, ts::String; <keyword arguments>)

Remove the tags ts from the indices of an ITensor or collection of indices.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

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ITensors.replacetagsMethod
replacetags[!](A::ITensor, tsold::String, tsnew::String; <keyword arguments>) -> ITensor

replacetags(is::IndexSet, tsold::String, tsnew::String; <keyword arguments>) -> IndexSet

Replace the tags tsold with tsnew for the indices of an ITensor.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

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ITensors.settagsMethod
settags[!](A::ITensor, ts::String; <keyword arguments>) -> ITensor

settags(is::IndexSet, ts::String; <keyword arguments>) -> IndexSet

Set the tags of the indices of an ITensor or IndexSet to ts.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

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ITensors.swaptagsMethod
swaptags[!](A::ITensor, ts1::String, ts2::String; <keyword arguments>) -> ITensor

swaptags(is::IndexSet, ts1::String, ts2::String; <keyword arguments>) -> IndexSet

Swap the tags ts1 with ts2 for the indices of an ITensor.

Optionally, only modify the indices with the specified keyword arguments.

Arguments

• tags = nothing: if specified, only modify Index i if hastags(i, tags) == true.
• plev = nothing: if specified, only modify Index i if hasplev(i, plev) == true.

The ITensor functions come in two versions, f and f!. The latter modifies the ITensor in-place. In both versions, the ITensor storage is not modified or copied (so it returns an ITensor with a view of the original storage).

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Index collections set operations

ITensors.commonindsFunction
commoninds(A, B; kwargs...)

Return a Vector with indices that are common between the indices of A and B (the set intersection, similar to Base.intersect).

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ITensors.uniqueindsFunction
uniqueinds(A, B; kwargs...)

Return Vector with indices that are unique to the set of indices of A and not in B (the set difference, similar to Base.setdiff).

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ITensors.noncommonindsFunction
noncommoninds(A, B; kwargs...)

Return a Vector with indices that are not common between the indices of A and B (the symmetric set difference, similar to Base.symdiff).

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ITensors.unionindsFunction
unioninds(A, B; kwargs...)

Return a Vector with indices that are the union of the indices of A and B (the set union, similar to Base.union).

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ITensors.hascommonindsFunction
hascommoninds(A, B; kwargs...)

hascommoninds(B; kwargs...) -> f::Function

Check if the ITensors or sets of indices A and B have common indices.

If only one ITensor or set of indices B is passed, return a function f such that f(A) = hascommoninds(A, B; kwargs...)

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Index Manipulations

ITensors.replaceindMethod
replaceind[!](A::ITensor, i1::Index, i2::Index) -> ITensor

Replace the Index i1 with the Index i2 in the ITensor.

The indices must have the same space (i.e. the same dimension and QNs, if applicable).

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ITensors.replaceindsMethod
replaceinds(A::ITensor, inds1, inds2) -> ITensor

replaceinds!(A::ITensor, inds1, inds2)

Replace the Index inds1[n] with the Index inds2[n] in the ITensor, where n runs from 1 to length(inds1) == length(inds2).

The indices must have the same space (i.e. the same dimension and QNs, if applicable).

The storage of the ITensor is not modified or copied (the output ITensor is a view of the input ITensor).

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ITensors.swapindMethod
swapind(A::ITensor, i1::Index, i2::Index) -> ITensor

swapind!(A::ITensor, i1::Index, i2::Index)

Swap the Index i1 with the Index i2 in the ITensor.

The indices must have the same space (i.e. the same dimension and QNs, if applicable).

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ITensors.swapindsMethod
swapinds(A::ITensor, inds1, inds2) -> ITensor

swapinds!(A::ITensor, inds1, inds2)

Swap the Index inds1[n] with the Index inds2[n] in the ITensor, where n runs from 1 to length(inds1) == length(inds2).

The indices must have the same space (i.e. the same dimension and QNs, if applicable).

The storage of the ITensor is not modified or copied (the output ITensor is a view of the input ITensor).

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Math operations

Base.:*Method
A::ITensor * B::ITensor
contract(A::ITensor, B::ITensor)

Contract ITensors A and B to obtain a new ITensor. This contraction * operator finds all matching indices common to A and B and sums over them, such that the result will have only the unique indices of A and B. To prevent indices from matching, their prime level or tags can be modified such that they no longer compare equal - for more information see the documentation on Index objects.

Examples

i = Index(2,"index_i"); j = Index(4,"index_j"); k = Index(3,"index_k")

A = randomITensor(i,j)
B = randomITensor(j,k)
C = A * B # contract over Index j

A = randomITensor(i,i')
B = randomITensor(i,i'')
C = A * B # contract over Index i

A = randomITensor(i)
B = randomITensor(j)
C = A * B # outer product of A and B, no contraction

A = randomITensor(i,j,k)
B = randomITensor(k,i,j)
C = A * B # inner product of A and B, all indices contracted
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Base.expMethod
exp(A::ITensor, Linds=Rinds', Rinds=inds(A,plev=0); ishermitian = false)

Compute the exponential of the tensor A by treating it as a matrix $A_{lr}$ with the left index l running over all indices in Linds and r running over all indices in Rinds.

Only accepts index lists Linds,Rinds such that: (1) length(Linds) + length(Rinds) == length(inds(A)) (2) length(Linds) == length(Rinds) (3) For each pair of indices (Linds[n],Rinds[n]), Linds[n] and Rinds[n] represent the same Hilbert space (the same QN structure in the QN case, or just the same length in the dense case), and appear in A with opposite directions.

When ishermitian=true the exponential of Hermitian(A_{lr}) is computed internally.

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Decompositions

LinearAlgebra.svdMethod
svd(A::ITensor, inds::Index...; <keyword arguments>)

Singular value decomposition (SVD) of an ITensor A, computed by treating the "left indices" provided collectively as a row index, and the remaining "right indices" as a column index (matricization of a tensor).

The first three return arguments are U, S, and V, such that A ≈ U * S * V.

Whether or not the SVD performs a trunction depends on the keyword arguments provided.

If the left or right set of indices are empty, all input indices are put on V or U respectively. To specify an empty set of left indices, you must explicitly use svd(A, ()) (svd(A) is currently undefined).

Examples

i = Index(2)
j = Index(5)
k = Index(2)

A = randomITensor(i, j, k)
U, S, V = svd(A, i, k);
@show norm(A - U * S * V) <= 10 * eps() * norm(A)

# This will truncate the last 2 singular values.
# The norm of the difference with the original tensor
# will be the sqrt root of the sum of the squares of the
# singular values that get truncated.
Utrunc, Strunc, Vtrunc = svd(A, i, k; maxdim=2);
@show norm(A - Utrunc * Strunc * Vtrunc) ≈ sqrt(S[3, 3]^2 + S[4, 4]^2)

# Alternatively we can specify that we want to truncate
# the weights of the singular values up to a certain cutoff,
# so the error will be no larger than the cutoff.
Utrunc2, Strunc2, Vtrunc2 = svd(A, i, k; cutoff=1e-10);
@show norm(A - Utrunc2 * Strunc2 * Vtrunc2) <= 1e-10

Keywords

• maxdim::Int: the maximum number of singular values to keep.
• mindim::Int: the minimum number of singular values to keep.
• cutoff::Float64: set the desired truncation error of the SVD, by default defined as the sum of the squares of the smallest singular values.
• lefttags::String = "Link,u": set the tags of the Index shared by U and S.
• righttags::String = "Link,v": set the tags of the Index shared by S and V.
• alg::String = "divide_and_conquer". Options:
• "divide_and_conquer" - A divide-and-conquer algorithm. LAPACK's gesdd. Fast, but may lead to some innacurate singular values for very ill-conditioned matrices. Also may sometimes fail to converge, leading to errors (in which case "qr_iteration" or "recursive" can be tried).
• "qr_iteration" - Typically slower but more accurate for very ill-conditioned matrices compared to "divide_and_conquer". LAPACK's gesvd.
• "recursive" - ITensor's custom svd. Very reliable, but may be slow if high precision is needed. To get an svd of a matrix A, an eigendecomposition of $A^{\dagger} A$ is used to compute U and then a qr of $A^{\dagger} U$ is used to compute V. This is performed recursively to compute small singular values.
• use_absolute_cutoff::Bool = false: set if all probability weights below the cutoff value should be discarded, rather than the sum of discarded weights.
• use_relative_cutoff::Bool = true: set if the singular values should be normalized for the sake of truncation.
• min_blockdim::Int = 0: for SVD of block-sparse or QN ITensors, require that the number of singular values kept be greater than or equal to this value when possible

See also: factorize, eigen

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LinearAlgebra.eigenMethod
eigen(A::ITensor[, Linds, Rinds]; <keyword arguments>)

Eigendecomposition of an ITensor A, computed by treating the "left indices" Linds provided collectively as a row index, and remaining "right indices" Rinds as a column index (matricization of a tensor).

If no indices are provided, pairs of primed and unprimed indices are searched for, with Linds taken to be the primed indices and Rinds taken to be the unprimed indices.

The return arguments are the eigenvalues D and eigenvectors U as tensors, such that A * U ∼ U * D (more precisely they are approximately equal up to proper replacements of indices, see the example for details).

Whether or not eigen performs a trunction depends on the keyword arguments provided. Note that truncation is only well defined for positive semidefinite matrices.

Arguments

• maxdim::Int: the maximum number of singular values to keep.
• mindim::Int: the minimum number of singular values to keep.
• cutoff::Float64: set the desired truncation error of the eigenvalues, by default defined as the sum of the squares of the smallest eigenvalues. For now truncation is only well defined for positive semi-definite eigenspectra.
• ishermitian::Bool = false: specify if the matrix is Hermitian, in which case a specialized diagonalization routine will be used and it is guaranteed that real eigenvalues will be returned.
• plev::Int = 0: set the prime level of the Indices of D. Default prime levels are subject to change.
• leftplev::Int = plev: set the prime level of the Index unique to D. Default prime levels are subject to change.
• rightplev::Int = leftplev+1: set the prime level of the Index shared by D and U. Default tags are subject to change.
• tags::String = "Link,eigen": set the tags of the Indices of D. Default tags are subject to change.
• lefttags::String = tags: set the tags of the Index unique to D. Default tags are subject to change.
• righttags::String = tags: set the tags of the Index shared by D and U. Default tags are subject to change.
• use_absolute_cutoff::Bool = false: set if all probability weights below the cutoff value should be discarded, rather than the sum of discarded weights.
• use_relative_cutoff::Bool = true: set if the singular values should be normalized for the sake of truncation.

Examples

i, j, k, l = Index(2, "i"), Index(2, "j"), Index(2, "k"), Index(2, "l")
A = randomITensor(i, j, k, l)
Linds = (i, k)
Rinds = (j, l)
D, U = eigen(A, Linds, Rinds)
dl, dr = uniqueind(D, U), commonind(D, U)
Ul = replaceinds(U, (Rinds..., dr) => (Linds..., dl))
A * U ≈ Ul * D # true

See also: svd, factorize

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LinearAlgebra.factorizeMethod
factorize(A::ITensor, Linds::Index...; <keyword arguments>)

Perform a factorization of A into ITensors L and R such that A ≈ L * R.

Arguments

• ortho::String = "left": Choose orthogonality properties of the factorization.
• "left": the left factor L is an orthogonal basis such that L * dag(prime(L, commonind(L,R))) ≈ I.
• "right": the right factor R forms an orthogonal basis.
• "none", neither of the factors form an orthogonal basis, and in general are made as symmetrically as possible (depending on the decomposition used).
• which_decomp::Union{String, Nothing} = nothing: choose what kind of decomposition is used.
• nothing: choose the decomposition automatically based on the other arguments. For example, when nothing is chosen and ortho = "left" or "right", and a cutoff is provided, svd or eigen is used depending on the provided cutoff (eigen is only used when the cutoff is greater than 1e-12, since it has a lower precision). When no truncation is requested qr is used for dense ITensors and svd for block-sparse ITensors (in the future qr will be used also for block-sparse ITensors in this case).
• "svd": L = U and R = S * V for ortho = "left", L = U * S and R = V for ortho = "right", and L = U * sqrt.(S) and R = sqrt.(S) * V for ortho = "none". To control which svd algorithm is choose, use the svd_alg keyword argument. See the documentation for svd for the supported algorithms, which are the same as those accepted by the alg keyword argument.
• "eigen": L = U and $R = U^{\dagger} A$ where U is determined from the eigendecompositon $A A^{\dagger} = U D U^{\dagger}$ for ortho = "left" (and vice versa for ortho = "right"). "eigen" is not supported for ortho = "none".
• "qr": L=Q and R an upper-triangular matrix when ortho = "left", and R = Q and L a lower-triangular matrix when ortho = "right" (currently supported for dense ITensors only).

In the future, other decompositions like QR (for block-sparse ITensors), polar, cholesky, LU, etc. are expected to be supported.

For truncation arguments, see: svd

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Memory operations

ITensors.permuteMethod
permute(T::ITensor, inds...; allow_alias = false)

Return a new ITensor T with indices permuted according to the input indices inds. The storage of the ITensor is permuted accordingly.

If called with allow_alias = true, it avoids copying data if possible. Therefore, it may return an alias of the input ITensor (an ITensor that shares the same data), such as if the permutation turns out to be trivial.

By default, allow_alias = false, and it never returns an alias of the input ITensor.

Examples

i = Index(2, "index_i"); j = Index(4, "index_j"); k = Index(3, "index_k");
T = randomITensor(i, j, k)

pT_1 = permute(T, k, i, j)
pT_2 = permute(T, j, i, k)

pT_noalias_1 = permute(T, i, j, k)
pT_noalias_1[1, 1, 1] = 12
T[1, 1, 1] != pT_noalias_1[1, 1, 1]

pT_noalias_2 = permute(T, i, j, k; allow_alias = false)
pT_noalias_2[1, 1, 1] = 12
T[1, 1, 1] != pT_noalias_1[1, 1, 1]

pT_alias = permute(T, i, j, k; allow_alias = true)
pT_alias[1, 1, 1] = 12
T[1, 1, 1] == pT_alias[1, 1, 1]
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