Algebra with YASTN tensors#

Basic algebra operations with symmetric tensors#

Symmetric tensors can be added and multiplied by a scalar through usual operations +, -, *, /. Element-wise raising to a power is done by the standard power operation **.

See examples: Basic algebra operations.

Simple element-wise operations#

Tensor.__abs__() → yastn.Tensor#

Return tensor with element-wise absolute values.

Can be on called on tensor as abs(tensor).

Tensor.real() → yastn.Tensor#

Return tensor with imaginary part set to zero.

Note

Follows the behavior of the backend.real() when it comes to creating a new copy of the data or handling datatype dtype.

Tensor.imag() → yastn.Tensor#

Return tensor with real part set to zero.

Note

Follows the behavior of the backend.imag() when it comes to creating a new copy of the data or handling datatype dtype.

Tensor.sqrt() → yastn.Tensor#

Return tensor after applying element-wise square root for each ternso element.

Tensor.rsqrt(cutoff=0) → yastn.Tensor#

Return element-wise operation 1/sqrt(tensor).

The tensor elements with absolute value below the cutoff are set to zero.

Parameters:

cutoff (real scalar) – (element-wise) cutoff for inversion

Tensor.reciprocal(cutoff=0) → yastn.Tensor#

Return element-wise operation 1/tensor.

The tensor elements with absolute value below the cutoff are set to zero.

Parameters:

cutoff (real scalar) – (element-wise) cutoff for inversion

Tensor.exp(step=1.0) → yastn.Tensor#

Return element-wise exp(step * tensor).

Note

This applies only to non-empty blocks of tensor

Tensor.__mul__(number) → yastn.Tensor#

Multiply tensor by a number, use: number * tensor.

Tensor.__pow__(exponent) → yastn.Tensor#

Element-wise exponent of tensor, use: tensor ** exponent.

Tensor.__truediv__(number) → yastn.Tensor#

Divide tensor by a scalar, use: tensor / number.

yastn.Tensor.__add__(a, b) → yastn.Tensor#

Add two tensors, use: \(a + b\).

Signatures and total charges should match.

yastn.Tensor.__sub__(a, b) → yastn.Tensor#

Subtract two tensors, use: \(a - b\).

Both signatures and total charges should match.

yastn.apxb(a, b, x=1) → yastn.Tensor[source]#

Directly compute the result of \(a + x b\).

Parameters:

  • a, b (yastn.Tensor)

  • x (number)

yastn.Tensor.__lt__(a, number) → yastn.Tensor[bool]#

Logical tensor with elements less-than a number (if it makes sense for backend data tensors), use: tensor < number

Intended for diagonal tensor to be applied as a truncation mask.

yastn.Tensor.__gt__(a, number) → yastn.Tensor[bool]#

Logical tensor with elements greater-than a number (if it makes sense for backend data tensors), use: tensor > number

Intended for diagonal tensor to be applied as a truncation mask.

yastn.Tensor.__le__(a, number) → yastn.Tensor[bool]#

Logical tensor with elements less-than-or-equal-to a number (if it makes sense for backend data tensors), use: tensor <= number

Intended for diagonal tensor to be applied as a truncation mask.

yastn.Tensor.__ge__(a, number) → yastn.Tensor[bool]#

Logical tensor with elements greater-than-or-equal-to a number (if it makes sense for backend data tensors), use: tensor >= number

Intended for diagonal tensor to be applied as a truncation mask.

yastn.Tensor.bitwise_not(a) → yastn.Tensor[bool]#

Return tensor after applying bitwise not on each tensor element.

Note

Operation applies only to non-empty blocks of tensor with tensor data dtype that allows for bitwise operation, i.e. intended for masks used to truncate tensor legs.

yastn.allclose(a, b, rtol=1e-13, atol=1e-13) → bool[source]#

Check if a and b are identical within a desired tolerance. To be True, all tensors’ blocks and merge history have to be identical. If this condition is satisfied, execute backend.allclose function to compare tensors’ data.

Note that if two tenors differ by zero blocks, the function returns False. To resolve such differences, use (a - b).norm() < tol

Parameters:

  • a, b (yastn.Tensor) – Tensor for comparison.

  • rtol, atol (float) – Desired relative and absolute precision.

Tensor contractions#

Tensor contractions are the main building block of tensor network algorithms. Functions below facilitate the computation of

  • Trace: \(B_{jl}= \sum_{i} T_{ijil}\) or using Einstein’s summation convention for repeated indices \(B_{jl} = T_{ijil}\).

  • Contractions: in the usual form \(C_{abc} = A_{aijb}{\times}B_{cij}\) and also outer products \(M_{abkl} = A_{ak}{\times}B_{bl}\)

or composition of such operations over several tensors.

See examples: Tensor contractions.

Tensor.__matmul__(b) → yastn.Tensor#

The operation A @ B uses @ operator to compute tensor dot product. The operation contracts the last axis of self, i.e., a, with the first axis of b.

It is equivalent to yastn.tensordot(a, b, axes=(a.ndim - 1, 0)).

yastn.tensordot(a, b, axes, conj=(0, 0)) → yastn.Tensor[source]#

Compute tensor dot product of two tensors along specified axes.

Outgoing legs are ordered such that first ones are the remaining legs of the first tensor in the original order, and than those of the second tensor.

Parameters:

  • a, b (yastn.Tensor) – Tensors to contract.

  • axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – legs of both tensors to be contracted (for each, they are specified by int or tuple of ints) e.g. axes=(0, 3) to contract 0th leg of a with 3rd leg of b; axes=((0, 3), (1, 2)) to contract legs 0 and 3 of a with 1 and 2 of b, respectively.

  • conj (tuple[int, int]) – specify tensors to conjugate by: (0, 0), (0, 1), (1, 0), or (1, 1). Default is (0, 0), i.e. neither tensor is conjugated.

yastn.vdot(a, b, conj=(1, 0)) → number[source]#

Compute scalar product \(\langle a|b \rangle\) between two tensors.

Parameters:

  • a, b (yastn.Tensor) – Two tensors for operation.

  • conj (tuple[int, int]) – shows which tensor to conjugate: (0, 0), (0, 1), (1, 0), or (1, 1). Default is (1, 0), i.e. tensor a is conjugated.

yastn.broadcast(a, *args, axes=0) → yastn.Tensor | iterable[yastn.Tensor][source]#

Compute tensordot product of diagonal tensor a with tensors in args.

Produce diagonal tensor if both are diagonal. Legs of the resulting tensors are ordered in the same way as those of tensors in args.

Parameters:

  • a, args (yastn.Tensor) – a is diagonal tensor to be broadcasted.

  • axes (int | Sequence[int]) – legs of tensors in args to be multiplied by diagonal tensor a. Number of tensors provided in args should match the length of axes.

yastn.apply_mask(a, *args, axes=0) → yastn.Tensor | iterable[yastn.Tensor][source]#

Apply mask given by nonzero elements of diagonal tensor a on specified axes of tensors in args. Number of tensors in args is not restricted. The length of the list axes has to be mathing with args.

Legs of resulting tensor are ordered in the same way as those of tensors in args. Bond dimensions of specified axes of args are truncated according to the mask a. Produce diagonal tensor if both are diagonal.

Parameters:

  • a, args (yastn.Tensor) – a is a diagonal tensor

  • axes (int | Sequence[int]) – leg of tensors in args where the mask is applied.

yastn.trace(a, axes=(0, 1)) → yastn.Tensor[source]#

Compute trace of legs specified by axes.

Parameters:

axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – Legs to be traced out, e.g axes=(0, 1); or axes=((2, 3, 4), (0, 1, 5)).

yastn.einsum(subscripts, *operands, order='Alphabetic') → yastn.Tensor[source]#

Execute series of tensor contractions.

Covering trace, tensordot (including outer products), and transpose. Follows notation of np.einsum() as close as possible.

Parameters:

  • subscripts (str)

  • operands (Sequence[yastn.Tensor])

  • order (str) – Specify order in which repeated indices from subscipt are contracted. By default it follows alphabetic order.

Example

yastn.einsum('*ij,jh->ih', t1, t2)

# matrix-matrix multiplication, where the first matrix is conjugated.
# Equivalent to

t1.conj() @ t2

yastn.einsum('ab,al,bm->lm', t1, t2, t3, order='ba')

# Contract along `b` first, and `a` second.
yastn.ncon(ts, inds, conjs=None) → yastn.Tensor[source]#

Execute series of tensor contractions.

Parameters:

  • ts (Sequence[yastn.Tensor]) – list of tensors to be contracted.

  • inds (Sequence[Sequence[int]]) – each inner tuple labels legs of respective tensor with integers. Positive values label legs to be contracted, with pairs of legs to be contracted denoted by the same integer label. Legs are contracted in the order of ascending integer value. Non-positive numbers label legs of the resulting tensor, in reversed order.

  • conjs (Sequence[int]) – For each tensor in ts contains either 0 or 1. If the value is 1, the tensor is conjugated.

Note

yastn.ncon() and yastn.einsum() differ only by syntax.

Example

# matrix-matrix multiplication where the first matrix is conjugated

yastn.ncon([a, b], ((-0, 1), (1, -1)), conjs=(1, 0))

# outer product

yastn.ncon([a, b], ((-0, -2), (-1, -3)))
yastn.swap_gate(a, axes, charge=None) → yastn.Tensor[source]#

Return tensor after application of a swap gate.

Multiply blocks with odd charges on swapped legs by -1.

Parameters:

  • axes (Sequence[int | Sequence[int]]) – Tuple with groups of legs. Consecutive pairs of grouped legs that are to be swapped. For instance, axes = (0, 1) apply swap gate between 0th and 1st leg. axes = ((0, 1), (2, 3), 4, 5) swaps (0, 1) with (2, 3), and 4 with 5.

  • charge (Optional[Sequence[int]]) – If provided, the swap gate is applied between a virtual one-dimensional leg of specified charge, e.g., a fermionic string, and tensor legs specified in axes. In this case, there is no application of a swap gates between legs specified in axes.

Transposition#

See examples: Transposition.

Tensor.transpose(axes=None) → yastn.Tensor#

Transpose tensor by permuting the order of its legs (spaces). Makes a shallow copy of tensor data if the order is not changed.

Parameters:

axes (Sequence[int]) – new order of legs. Has to be a valid permutation of (0, 1, ..., ndim-1) where ndim is tensor order (number of legs). By default is range(a.ndim)[::-1], which reverses the order of the axes.

property Tensor.T: yastn.Tensor#

Same as self.transpose().

Tensor.move_leg(source, destination) → yastn.Tensor#

Change the position of an axis (or a group of axes) of the tensor. This is a convenience function for subset of possible permutations. It computes the corresponding permutation and then calls yastn.Tensor.transpose().

Makes a shallow copy of tensor data if the order is not changed.

Parameters:

source, destination (int | Sequence[int])

Tensor.moveaxis(source, destination) → yastn.Tensor#

Change the position of an axis (or a group of axes) of the tensor. This is a convenience function for subset of possible permutations. It computes the corresponding permutation and then calls yastn.Tensor.transpose().

This function is an alias for yastn.Tensor.move_leg().

Parameters:

source, destination (int | Sequence[int])

Fusion of legs (reshaping)#

Fusion of several vector spaces \(V_1,V_2,\ldots,V_n\) creates a new vector space as direct product \(W=V_1 \otimes V_2 \otimes \ldots \otimes V_n\), which is then indexed by a single index of dimension \(\prod_i {\rm dim}(V_i)\). Here multiplication depends on abelian symmetry, as the resulting total dimension is a sum of dimensions for effective charges. The inverse operation can split the fused space into its original constituents.

For dense tensors, the operation corresponds to reshaping.

Fusion can be used to vary compression between (unfused) symmetric tensors with many small non-zero blocks and tensors with several fused spaces having just few, but large non-zero blocks.

See examples: Fusion (reshaping).

Tensor.fuse_legs(axes, mode=None) → yastn.Tensor#

Fuse groups of legs into effective legs, reducing the rank of the tensor.

Note

Fusion can be reverted back by yastn.Tensor.unfuse_legs()

First, the legs are permuted into desired order. Then, selected groups of consecutive legs are fused. The desired order of the legs is given by a tuple axes of leg indices where the groups of legs to be fused are denoted by inner tuples

axes=(0,1,(2,3,4),5)  keep leg order, fuse legs (2,3,4) into new leg
->   (0,1, 2,     3)
    __              __
0--|  |--3      0--|  |--3<-5
1--|  |--4  =>  1--|__|
2--|__|--5          |
                    2<-(2,3,4)


axes=(2,(3,1),4,(7,6),5,0)  permute indices, then fuse legs (3,1) into new leg
->   (0, 1,   2, 3,   4,5)  and legs (7,6) into another new leg
    __                   __
0--|  |--4        0->5--|  |--2<-4
1--|  |--5 =>           |  |--4<-5
2--|  |--6        2->0--|  |--3<-(7,6)
3--|__|--7    (3,1)->1--|__|

Two types of fusion are supported: meta and hard:

  • 'meta' performs the fusion only at the level of syntax, where it operates as a tensor with lower rank. Tensor structure and data (blocks) are not affected - apart from a transpose that may be needed for consistency.

  • 'hard' changes both the structure and data by aggregating smaller blocks into larger ones. Such fusion allows to balance number of non-zero blocks and typical block size.

It is possible to use both meta and hard fusion of legs on the same tensor. Applying hard fusion on tensor turns all previous meta fused legs into hard fused ones.

Parameters:

  • axes (Sequence[int | Sequence[int]]) – tuple of leg indices. Groups of legs to be fused together are accumulated within inner tuples.

  • mode (str) – can select 'hard' or 'meta' fusion. If None, uses default_fusion from tensor’s configuration. Configuration option force_fusion can be used to override mode (introduced for debugging purposes).

Tensor.unfuse_legs(axes) → yastn.Tensor#

Unfuse legs, reverting one layer of fusion.

If the tensor has been obtained by fusing some legs together, unfuse_legs can revert such fusion. The legs to be unfused are passed in axes as int or tuple[int] in case of more legs to be unfused. The unfused legs are inserted at the positions of the fused legs. The remaining legs are shifted accordingly

axes=2              unfuse leg 2 into legs 2,3,4
->   (0,1,2,3,4,5)
    __                 __
0--|  |--3         0--|  |--3
1--|__|        =>  1--|  |--4
    |              2--|__|--5<-3
    2=(2,3,4)


axes=(    2,      5  )  unfuse leg 2 into legs 2,3 and leg 5 into legs 6,7
->   (0,1,2,3,4,5,6,7)
          __                   __
      0--|  |--3           0--|  |--4
         |  |--4       =>  1--|  |--5
      1--|  |--5=(6,7)     2--|  |--6
(2,3)=2--|__|              3--|__|--7

Unfusing a leg obtained by fusing together other previously fused legs, unfuses only the last fusion

axes=2              unfuse leg 2 into legs 2,3
->   (0,1,2,3,4)
    __                    __
0--|  |--3            0--|  |--3=(3,4)
1--|__|           =>  1--|  |
    |                 2--|__|--4<-3
    2=(2,3=(3,4))

fuse_legs may involve leg transposition, which is not undone by unfuse_legs.

Parameters:

axes (int | Sequence[int]) – leg(s) to unfuse.

Tensor.add_leg(axis=-1, s=-1, t=None, leg=None) → yastn.Tensor#

Creates a new tensor with an extra leg that carries the charge (or part of it) of the orignal tensor. This is achieved by the extra leg having a single charge sector of dimension D=1. The total charge of the tensor yastn.Tensor.n can be modified this way.

Makes a shallow copy of tensor data.

Parameters:

  • axis (int) – index of the new leg

  • s (int) – signature of the new leg, +1 or -1. The default is -1, where the leg charge is equal to the tensor charge for t=None.

  • t (int | Sequence[int]) – charge carried by the new leg. If None, takes the total charge n of the original tensor resulting in a tensor with n=0.

  • leg (Optional[Leg]) – It is possible to provide a new leg directly. It has to be of dimension one but can contain information about the fusion of other dimension-one legs. If given (not None), it overrides information provided in s and t.

Tensor.remove_leg(axis=-1) → yastn.Tensor#

Removes leg with a single charge sector of dimension one from tensor. The charge carried by that leg (if any) is added to the tensor’s total charge yastn.Tensor.n.

Makes a shallow copy of tensor data.

Parameters:

axis (int) – index of the leg to be removed.

Tensor.drop_leg_history(axes=None) → yastn.Tensor#

Drops information about original structure of fused or blocked legs that have been combined into a selected tensor leg(s).

Makes a shallow copy of tensor data.

Parameters:

axes (int | Sequence[int]) – index of the leg, or a group of legs. If None, drops information from all legs.

Conjugation of symmetric tensors#

See examples: Conjugation of symmetric tensors.

Tensor.conj() → yastn.Tensor#

Return conjugated tensor. In particular, change the sign of the signature s to -s, the total charge n to -n, and complex conjugate each block of the tensor.

Follows the behavior of the backend.conj() when it comes to creating a new copy of the data.

Tensor.conj_blocks() → yastn.Tensor#

Complex-conjugate all blocks leaving symmetry structure (signature, blocks charge, and total charge) unchanged.

Follows the behavior of the backend.conj() when it comes to creating a new copy of the data.

Tensor.flip_signature() → yastn.Tensor#

Change the signature of the tensor, s to -s or equivalently reverse the direction of in- and out-going legs, and also the total charge of the tensor n to -n. Does not complex-conjugate the elements of the tensor.

Creates a shallow copy of the data.

Tensor.flip_charges(axes=None) → yastn.Tensor#

Flip signs of charges and signatures on specified legs.

Flipping charges/signature of hard-fused legs is not supported.

Parameters:

axes (int | Sequence[int]) – index of the leg, or a group of legs. If None, flips all legs.

Tensor norms#

yastn.linalg.norm(a, p='fro') → number#

Norm of the tensor.

Parameters:

p (str) – 'fro' for Frobenius norm; 'inf' for \(l^\infty\) (or supremum) norm.

Spectral decompositions and truncation#

yastn.linalg.svd(a, axes=(0, 1), sU=1, nU=True, compute_uv=True, Uaxis=-1, Vaxis=0, policy='fullrank', fix_signs=False, svd_on_cpu=False, **kwargs) → tuple[yastn.Tensor, yastn.Tensor, yastn.Tensor] | yastn.Tensor#

Split tensor into \(a = U S V\) using exact singular value decomposition (SVD), where the columns of U and the rows of V form orthonormal bases and S is a positive and diagonal matrix.

Parameters:

  • axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – Specify two groups of legs between which to perform SVD, as well as their final order.

  • sU (int) – Signature of the new leg in U; equal to 1 or -1. The default is 1. V is going to have the opposite signature on the connecting leg.

  • nU (bool) – Whether or not to attach the charge of a to U. If False, it is attached to V. The default is True.

  • compute_uv (bool) – If True, compute U and V in addition to S and only S otherwise. The default is True.

  • Uaxis, Vaxis (int) – Specify which leg of U and V tensors are connecting with S. By default, it is the last leg of U and the first of V, in which case a = U @ S @ V.

  • policy (str) – "fullrank" or "lowrank" are allowed. Use standard full (but reduced) SVD for "fullrank". For "lowrank", uses randomized/truncated SVD and requires providing D_block in kwargs.

  • fix_signs (bool) – Whether or not to fix phases in U and V, so that the largest element in each column of U is positive. Provide uniqueness of decomposition for non-degenerate cases. The default is False.

  • svd_on_cpu (bool) – GPU tends to be very slow when executing SVD. If True, the data will be copied to CPU for SVD, and the results will be copied back to the device. Nothing is done for data already residing on CPU. The default is False.

Returns:

The first option, with compute_uv=True, is default.

Return type:

U, S, V or S

yastn.linalg.svd_with_truncation(a, axes=(0, 1), sU=1, nU=True, Uaxis=-1, Vaxis=0, policy='fullrank', fix_signs=False, svd_on_cpu=False, tol=0, tol_block=0, D_block=inf, D_total=inf, truncate_multiplets=False, mask_f=None, **kwargs) → tuple[yastn.Tensor, yastn.Tensor, yastn.Tensor]#

Split tensor using exact singular value decomposition (SVD) into \(a = U S V\), where the columns of U and the rows of V form orthonormal bases and S is positive and diagonal matrix.

The function allows for optional truncation. Truncation can be based on relative tolerance, bond dimension of each block, and total bond dimension across all blocks (whichever gives smaller total dimension).

Charge of input tensor a is attached to U if nU=True and to V otherwise.

Parameters:

  • axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – Specify two groups of legs between which to perform SVD, as well as their final order.

  • sU (int) – signature of the new leg in U; equal 1 or -1. The default is 1. V is going to have opposite signature on connecting leg.

  • nU (bool) – Whether or not to attach the charge of a to U. If False, it is attached to V. The default is True.

  • Uaxis, Vaxis (int) – specify which leg of U and V tensors are connecting with S. By default, it is the last leg of U and the first of V.

  • policy (str) – "fullrank" or "lowrank" are allowed. For "fullrank" use standard full (but reduced) SVD, and for "lowrank" use randomized/truncated SVD and requires providing D_block in kwargs.

  • tol (float) – relative tolerance of singular values below which to truncate across all blocks.

  • tol_block (float) – relative tolerance of singular values below which to truncate within individual blocks.

  • D_block (int) – largest number of singular values to keep in a single block.

  • D_total (int) – largest total number of singular values to keep.

  • truncate_multiplets (bool) – If True, enlarge the truncation range specified by other arguments by shifting the cut to the largest gap between to-be-truncated singular values across all blocks. It provides a heuristic mechanism to avoid truncating part of a multiplet. The default is False.

  • mask_f (function[yastn.Tensor] -> yastn.Tensor) – custom truncation-mask function. If provided, it overrides all other truncation-related arguments.

Return type:

U, S, V

yastn.linalg.qr(a, axes=(0, 1), sQ=1, Qaxis=-1, Raxis=0) → tuple[yastn.Tensor, yastn.Tensor]#

Split tensor using reduced QR decomposition, such that \(a = Q R\), with \(QQ^\dagger=I\). The charge of R is zero.

Parameters:

  • axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – Specify two groups of legs between which to perform QR, as well as their final order.

  • sQ (int) – signature of connecting leg in Q; equal 1 or -1. The default is 1. R is going to have opposite signature on connecting leg.

  • Qaxis, Raxis (int) – specify which leg of Q and R tensors are connecting to the other tensor. By default, it is the last leg of Q and the first leg of R.

Return type:

Q, R

yastn.linalg.eigh(a, axes, sU=1, Uaxis=-1) → tuple[yastn.Tensor, yastn.Tensor]#

Split symmetric tensor using exact eigenvalue decomposition, \(a= USU^{\dagger}\).

Tensor is expected to be symmetric (hermitian) with total charge 0.

Parameters:

  • axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – Specify two groups of legs between which to perform svd, as well as their final order.

  • sU (int) – signature of connecting leg in U equall 1 or -1. The default is 1.

  • Uaxis (int) – specify which leg of U is the new connecting leg. By default, it is the last leg.

Return type:

S, U

yastn.linalg.eigh_with_truncation(a, axes, sU=1, Uaxis=-1, tol=0, tol_block=0, D_block=inf, D_total=inf, truncate_multiplets=False) → tuple[yastn.Tensor, yastn.Tensor]#

Split symmetric tensor using exact eigenvalue decomposition, \(a= USU^{\dagger}\). Optionally, truncate the resulting decomposition.

Tensor is expected to be symmetric (hermitian) with total charge 0. Truncation can be based on relative tolerance, bond dimension of each block, and total bond dimension across all blocks (whichever gives smaller total dimension). Truncate based on tolerance only if some eigenvalues are positive – than all negative ones are discarded.

Parameters:

  • axes (tuple[int, int] | tuple[Sequence[int], Sequence[int]]) – Specify two groups of legs between which to perform svd, as well as their final order.

  • sU (int) – signature of connecting leg in U equall 1 or -1. The default is 1.

  • Uaxis (int) – specify which leg of U is the new connecting leg. By default, it is the last leg.

  • tol (float) – relative tolerance of singular values below which to truncate across all blocks.

  • tol_block (float) – relative tolerance of singular values below which to truncate within individual blocks.

  • D_block (int) – largest number of singular values to keep in a single block.

  • D_total (int) – largest total number of singular values to keep.

Return type:

S, U

yastn.linalg.truncation_mask(S, tol=0, tol_block=0, D_block=inf, D_total=inf, truncate_multiplets=False, **kwargs) → yastn.Tensor[bool]#

Generate mask tensor based on diagonal and real tensor S. It can be then used for truncation.

Per block options D_block and tol_block govern truncation within individual blocks, keeping at most D_block values which are larger than relative cutoff tol_block.

Parameters:

  • S (yastn.Tensor) – Diagonal tensor with spectrum.

  • tol (float) – relative tolerance.

  • tol_block (float) – relative tolerance per block.

  • D_total (int) – maximum number of elements kept across all blocks.

  • D_block (int) – maximum number of elements kept per block.

  • truncate_multiplets (bool) – If True, enlarge the truncation range specified by other arguments by shifting the cut to the largest gap between to-be-truncated singular values across all blocks. It provides a heuristic mechanism to avoid truncating part of a multiplet. If True, tol_block and D_block are ignored, as truncate_multiplets is a global condition. The default is False.

yastn.linalg.truncation_mask_multiplets(S, tol=0, D_total=inf, eps_multiplet=1e-13, **kwargs) → yastn.Tensor[bool]#

Generate a mask tensor from real positive spectrum S, while preserving degenerate multiplets. This is achieved by truncating the spectrum at the boundary between multiplets.

Parameters:

  • S (yastn.Tensor) – Diagonal tensor with spectrum.

  • tol (float) – relative tolerance.

  • D_total (int) – maximum number of elements kept in the result.

  • eps_multiplet (float) – relative tolerance on multiplet splitting. If relative difference between two consecutive elements of S is larger than eps_multiplet, these elements are not considered as part of the same multiplet.

yastn.linalg.entropy(a, alpha=1, tol=1e-12) → number#

Calculate entropy from probabilities encoded in diagonal tensor a.

Normalizes (sum of) a to 1, but do not check correctness otherwise. Use base-2 log. For empty or zero tensor, returns 0.

Parameters:

  • alpha (float) – Order of Renyi entropy. alpha == 1 is von Neuman entropy: \(-{\rm Tr}(a {\rm log2}(a))\) otherwise: \(1/(1-alpha) {\rm log2}({\rm Tr}(a ** alpha))\)

  • tol (float) – Discard all probabilities smaller than tol during calculation.

Auxliary#

Methods called by Krylov-based algorithms.

Tensor.expand_krylov_space(f, tol, ncv, hermitian, V, H=None, **kwargs)#

Expand the Krylov base up to ncv states or until reaching desired tolerance tol. Implementation for yastn.Tensor.

Tensor.linear_combination(*vectors, amplitudes, **kwargs)#

Linear combination of yastn.Tensors() with given amplitudes.