quimb.linalg.base_linalg

Backend agnostic functions for solving matrices either fully or partially.

Attributes

Classes

qarray

Thin subclass of numpy.ndarray with some convenient quantum

IdentityLinearOperator

Get a LinearOperator representation of the identity operator,

Lazy

A simple class representing an unconstructed matrix. This can be passed

Functions

raise_cant_find_library_function(x[, extra_msg])

Return function to flag up a missing necessary library.

dag(qob)

Conjugate transpose.

issparse(qob)

Checks if qob is explicitly sparse.

isdense(qob)

Checks if qob is explicitly dense.

vdot(a, b)

Accelerated 'Hermitian' inner product of two arrays. In other words,

ldmul(diag, mat)

Accelerated left diagonal multiplication. Equivalent to

eig_numpy(A[, sort, isherm, return_vecs, autoblock])

Numpy based dense eigensolve.

eigs_numpy(A, k[, B, which, return_vecs, sigma, ...])

Partial eigen-decomposition using numpy's dense linear algebra.

svds_numpy(a, k[, return_vecs])

Partial singular value decomposition using numpys (full) singular value

eigs_scipy(A, k, *[, B, which, return_vecs, sigma, ...])

Returns a few eigenpairs from a possibly sparse hermitian operator

eigs_lobpcg(A, k, *[, B, v0, which, return_vecs, ...])

Interface to scipy's lobpcg eigensolver, which can be good for

svds_scipy(A[, k, return_vecs, backend])

Compute a number of singular value pairs

choose_backend(A, k[, int_eps, B])

Pick a backend automatically for partial decompositions.

eigensystem_partial(A, k, isherm, *[, B, which, ...])

Return a few eigenpairs from an operator.

eigensystem(A, isherm, *[, k, sort, return_vecs])

Find all or some eigenpairs of an operator.

eigenvectors(A, isherm, *[, sort])

groundstate(ham, **kwargs)

Alias for finding lowest eigenvector only.

groundenergy(ham, **kwargs)

Alias for finding lowest eigenvalue only.

bound_spectrum(A[, backend])

Return the smallest and largest eigenvalue of hermitian operator A.

_rel_window_to_abs_window(el_min, el_max, w_0[, w_sz])

Convert min/max eigenvalues and relative window to absolute values.

eigh_window(A, w_0, k[, w_sz, backend, return_vecs, ...])

Return mid-spectrum eigenpairs from a hermitian operator.

eigvalsh_window(*args, **kwargs)

Alias for only finding the eigenvalues in a relative window.

eigvecsh_window(*args, **kwargs)

Alias for only finding the eigenvectors in a relative window.

svd(A[, return_vecs])

Compute full singular value decomposition of an operator, using numpy.

svds(A, k[, ncv, return_vecs, backend])

Compute the partial singular value decomposition of an operator.

norm_2(A, **kwargs)

Return the 2-norm of operator, A, i.e. the largest singular value.

norm_fro_dense(A)

Frobenius norm for dense matrices

norm_fro_sparse(A)

norm_trace_dense(A[, isherm])

Returns the trace norm of operator A, that is,

norm(A[, ntype])

Operator norms.

expm(A[, herm])

Matrix exponential, can be accelerated if explicitly hermitian.

expm_multiply(mat, vec[, backend])

Compute the action of expm(mat) on vec.

sqrtm(A[, herm])

Matrix square root, can be accelerated if explicitly hermitian.

Module Contents

quimb.linalg.base_linalg.raise_cant_find_library_function(x, extra_msg=None)[source]

Return function to flag up a missing necessary library.

This is simplify the task of flagging optional dependencies only at the point at which they are needed, and not earlier.

Parameters:
  • x (str) – Name of library

  • extra_msg (str, optional) – Make the function print this message as well, for additional information.

Returns:

A mock function that when called, raises an import error specifying the required library.

Return type:

callable

class quimb.linalg.base_linalg.qarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)[source]

Bases: numpy.ndarray

Thin subclass of numpy.ndarray with some convenient quantum linear algebra related methods and attributes (.H, &, etc.), and matrix-like preservation of at least 2-dimensions so as to distiguish kets and bras.

property H
property A
__array__()[source]
__and__(other)[source]
normalize(inplace=True)[source]
nmlz(inplace=True)[source]
chop(inplace=True)[source]
tr()[source]
partial_trace(dims, keep)[source]
ptr(dims, keep)[source]
__str__()[source]

Return str(self).

__repr__()[source]

Return repr(self).

quimb.linalg.base_linalg.dag(qob)[source]

Conjugate transpose.

quimb.linalg.base_linalg.issparse(qob)[source]

Checks if qob is explicitly sparse.

quimb.linalg.base_linalg.isdense(qob)[source]

Checks if qob is explicitly dense.

quimb.linalg.base_linalg.vdot(a, b)[source]

Accelerated ‘Hermitian’ inner product of two arrays. In other words, b here will be conjugated by the function.

quimb.linalg.base_linalg.ldmul(diag, mat)[source]

Accelerated left diagonal multiplication. Equivalent to numpy.diag(diag) @ mat, but faster than numpy.

Parameters:
  • diag (vector or 1d-array) – Vector representing the diagonal of a matrix.

  • mat (dense or sparse matrix) – A normal (non-diagonal) matrix.

Returns:

Dot product of the matrix whose diagonal is diag and mat.

Return type:

dense or sparse matrix

quimb.linalg.base_linalg.eig_numpy(A, sort=True, isherm=True, return_vecs=True, autoblock=False)[source]

Numpy based dense eigensolve.

Parameters:
  • A (array_like) – The operator to decompose.

  • sort (bool, optional) – Whether to sort into ascending order.

  • isherm (bool, optional) – Whether A is hermitian.

  • return_vecs (bool, optional) – Whether to return the eigenvectors.

  • autoblock (bool, optional) – If true, automatically identify and exploit symmetries appearing in the current basis as block diagonals formed via permutation of rows and columns.

Returns:

  • evals (1D-array) – The eigenvalues.

  • evecs (qarray) – If return_vecs=True, the eigenvectors.

quimb.linalg.base_linalg.eigs_numpy(A, k, B=None, which=None, return_vecs=True, sigma=None, isherm=True, P=None, sort=True, **eig_opts)[source]

Partial eigen-decomposition using numpy’s dense linear algebra.

Parameters:
  • A (array_like or quimb.Lazy) – Operator to partially eigen-decompose.

  • k (int) – Number of eigenpairs to return.

  • B (array_like or quimb.Lazy) – If given, the RHS operator defining a generalized eigen problem.

  • which (str, optional) – Which part of the spectrum to target.

  • return_vecs (bool, optional) – Whether to return eigenvectors.

  • sigma (None or float, optional) – Target eigenvalue.

  • isherm (bool, optional) – Whether a is hermitian.

  • P (array_like or quimb.Lazy) – Perform the eigensolve in the subspace defined by this projector.

  • sort (bool, optional) – Whether to sort reduced list of eigenpairs into ascending order.

  • eig_opts – Settings to pass to numpy.eig… functions.

Returns:

lk, (vk)

Return type:

k eigenvalues (and eigenvectors) sorted according to which

quimb.linalg.base_linalg.svds_numpy(a, k, return_vecs=True, **_)[source]

Partial singular value decomposition using numpys (full) singular value decomposition.

Parameters:
  • a (array_like) – Operator to decompose.

  • k (int, optional) – Number of singular value triplets to retrieve.

  • return_vecs (bool, optional) – whether to return the computed vecs or values only

Returns:

Singlar value triplets.

Return type:

(uk,) sk (, vkt)

quimb.linalg.base_linalg.eigs_scipy(A, k, *, B=None, which=None, return_vecs=True, sigma=None, isherm=True, sort=True, P=None, tol=None, backend=None, **eigs_opts)[source]

Returns a few eigenpairs from a possibly sparse hermitian operator

Parameters:
  • A (array_like, sparse_matrix, LinearOperator or quimb.Lazy) – The operator to solve for.

  • k (int) – Number of eigenpairs to return

  • B (array_like, sparse_matrix, LinearOperator or quimb.Lazy, optional) – If given, the RHS operator (which should be positive) defining a generalized eigen problem.

  • which (str, optional) – where in spectrum to take eigenvalues from (see scipy.sparse.linalg.eigsh()).

  • return_vecs (bool, optional) – Whether to return the eigenvectors as well.

  • sigma (float, optional) – Shift, if targeting interior eigenpairs.

  • isherm (bool, optional) – Whether A is hermitian.

  • P (array_like, sparse_matrix, LinearOperator or quimb.Lazy, optional) – Perform the eigensolve in the subspace defined by this projector.

  • sort (bool, optional) – Whether to ensure the eigenvalues are sorted in ascending value.

  • backend (None or 'primme', optional) – Which backend to use.

  • eigs_opts – Supplied to scipy.sparse.linalg.eigsh() or scipy.sparse.linalg.eigs().

Returns:

  • lk ((k,) array) – The eigenvalues.

  • vk ((m, k) array) – Corresponding eigenvectors (if return_vecs=True).

quimb.linalg.base_linalg.eigs_lobpcg(A, k, *, B=None, v0=None, which=None, return_vecs=True, sigma=None, isherm=True, P=None, sort=True, **lobpcg_opts)[source]

Interface to scipy’s lobpcg eigensolver, which can be good for generalized eigenproblems with matrix-free operators. Seems to a be a bit innacurate though (e.g. on the order of ~ 1e-6 for eigenvalues). Also only takes real, symmetric problems, targeting smallest eigenvalues (though scipy will soon have complex support, and its easy to add oneself).

Note that the slepc eigensolver also has a lobpcg backend (EPSType='lobpcg') which accepts complex input and is more accurate - though seems slower.

Parameters:
  • A (array_like, sparse_matrix, LinearOperator or callable) – The operator to solve for.

  • k (int) – Number of eigenpairs to return

  • B (array_like, sparse_matrix, LinearOperator or callable, optional) – If given, the RHS operator (which should be positive) defining a generalized eigen problem.

  • v0 (array_like (d, k), optional) – The initial subspace to iterate with.

  • which ({'SA', 'LA'}, optional) – Find the smallest or largest eigenvalues.

  • return_vecs (bool, optional) – Whether to return the eigenvectors found.

  • P (array_like, sparse_matrix, LinearOperator or callable, optional) – Perform the eigensolve in the subspace defined by this projector.

  • sort (bool, optional) – Whether to ensure the eigenvalues are sorted in ascending value.

  • lobpcg_opts – Supplied to scipy.sparse.linagl.lobpcg().

Returns:

  • lk (array_like (k,)) – The eigenvalues.

  • vk (array_like (d, k)) – The eigenvectors, if return_vecs=True.

quimb.linalg.base_linalg.eigs_primme
quimb.linalg.base_linalg.svds_scipy(A, k=6, *, return_vecs=True, backend=None, **svds_opts)[source]

Compute a number of singular value pairs

Parameters:
  • A ((m, n) dense, sparse or linear operator.) – The operator to solve.

  • k (int) – Number of requested singular values.

  • return_vecs (bool, optional) – Whether to return the singular vectors.

Returns:

  • U ((m, k) array) – Left singular vectors (if return_vecs=True) as columns.

  • s ((k,) array) – Singular values.

  • VH ((k, n) array) – Right singular vectors (if return_vecs=True) as rows.

quimb.linalg.base_linalg.svds_primme
quimb.linalg.base_linalg.SLEPC4PY_FOUND
quimb.linalg.base_linalg.eigs_slepc[source]
quimb.linalg.base_linalg.choose_backend(A, k, int_eps=False, B=None)[source]

Pick a backend automatically for partial decompositions.

quimb.linalg.base_linalg._EIGS_METHODS
quimb.linalg.base_linalg.eigensystem_partial(A, k, isherm, *, B=None, which=None, return_vecs=True, sigma=None, ncv=None, tol=None, v0=None, sort=True, backend=None, fallback_to_scipy=False, **backend_opts)[source]

Return a few eigenpairs from an operator.

Parameters:
  • A (sparse, dense or linear operator) – The operator to solve for.

  • k (int) – Number of eigenpairs to return.

  • isherm (bool) – Whether to use hermitian solve or not.

  • B (sparse, dense or linear operator, optional) – If given, the RHS operator defining a generalized eigen problem.

  • which ({'SA', 'LA', 'LM', 'SM', 'TR'}) – Where in spectrum to take eigenvalues from (see :func:scipy.sparse.linalg.eigsh)

  • return_vecs (bool, optional) – Whether to return the eigenvectors.

  • sigma (float, optional) – Which part of spectrum to target, implies which=’TR’ if which is None.

  • ncv (int, optional) – number of lanczos vectors, can use to optimise speed

  • tol (None or float) – Tolerance with which to find eigenvalues.

  • v0 (None or 1D-array like) – An initial vector guess to iterate with.

  • sort (bool, optional) – Whether to explicitly sort by ascending eigenvalue order.

  • backend ({'AUTO', 'NUMPY', 'SCIPY',) – ‘LOBPCG’, ‘SLEPC’, ‘SLEPC-NOMPI’}, optional Which solver to use.

  • fallback_to_scipy (bool, optional) – If an error occurs and scipy is not being used, try using scipy.

  • backend_opts – Supplied to the backend solver.

Returns:

  • elk ((k,) array) – The k eigenvalues.

  • evk ((d, k) array) – Array with k eigenvectors as columns if return_vecs.

quimb.linalg.base_linalg.eigensystem(A, isherm, *, k=-1, sort=True, return_vecs=True, **kwargs)[source]

Find all or some eigenpairs of an operator.

Parameters:
  • A (operator) – The operator to decompose.

  • isherm (bool) – Whether the operator is assumed to be hermitian or not.

  • k (int, optional) – If negative, find all eigenpairs, else perform partial eigendecomposition and find k pairs. See eigensystem_partial().

  • sort (bool, optional) – Whether to sort the eigenpairs in ascending eigenvalue order.

  • kwargs – Supplied to the backend function.

Returns:

  • el ((k,) array) – Eigenvalues.

  • ev ((d, k) array) – Corresponding eigenvectors as columns of array, such that ev @ diag(el) @ ev.H == A.

quimb.linalg.base_linalg.eig
quimb.linalg.base_linalg.eigh
quimb.linalg.base_linalg.eigvals
quimb.linalg.base_linalg.eigvalsh
quimb.linalg.base_linalg.eigenvectors(A, isherm, *, sort=True, **kwargs)[source]
quimb.linalg.base_linalg.eigvecs
quimb.linalg.base_linalg.eigvecsh
quimb.linalg.base_linalg.groundstate(ham, **kwargs)[source]

Alias for finding lowest eigenvector only.

quimb.linalg.base_linalg.groundenergy(ham, **kwargs)[source]

Alias for finding lowest eigenvalue only.

quimb.linalg.base_linalg.bound_spectrum(A, backend='auto', **kwargs)[source]

Return the smallest and largest eigenvalue of hermitian operator A.

quimb.linalg.base_linalg._rel_window_to_abs_window(el_min, el_max, w_0, w_sz=None)[source]

Convert min/max eigenvalues and relative window to absolute values.

Parameters:
  • el_min (float) – Smallest eigenvalue.

  • el_max (float) – Largest eigenvalue.

  • w_0 (float [0.0 - 1.0]) – Relative window centre.

  • w_sz (float, optional) – Relative window width.

Returns:

Absolute value of centre of window, lower and upper intervals if a window size is specified.

Return type:

l_0[, l_min, l_max]

quimb.linalg.base_linalg.eigh_window(A, w_0, k, w_sz=None, backend='AUTO', return_vecs=True, offset_const=1 / 104729, **kwargs)[source]

Return mid-spectrum eigenpairs from a hermitian operator.

Parameters:
  • A ((d, d) operator) – Operator to retrieve eigenpairs from.

  • w_0 (float [0.0, 1.0]) – Relative window centre to retrieve eigenpairs from.

  • k (int) – Target number of eigenpairs to retrieve.

  • w_sz (float, optional) – Relative maximum window width within which to keep eigenpairs.

  • backend (str, optional) – Which eigh() backend to use.

  • return_vecs (bool, optional) – Whether to return eigenvectors as well.

  • offset_const (float, optional) – Small fudge factor (relative to window range) to avoid 1 / 0 issues.

Returns:

  • el ((k,) array) – Eigenvalues around w_0.

  • ev ((d, k) array) – The eigenvectors, if return_vecs=True.

quimb.linalg.base_linalg.eigvalsh_window(*args, **kwargs)[source]

Alias for only finding the eigenvalues in a relative window.

quimb.linalg.base_linalg.eigvecsh_window(*args, **kwargs)[source]

Alias for only finding the eigenvectors in a relative window.

quimb.linalg.base_linalg.svd(A, return_vecs=True)[source]

Compute full singular value decomposition of an operator, using numpy.

Parameters:
  • A ((m, n) array) – The operator.

  • return_vecs (bool, optional) – Whether to return the singular vectors.

Returns:

  • U ((m, k) array) – Left singular vectors (if return_vecs=True) as columns.

  • s ((k,) array) – Singular values.

  • VH ((k, n) array) – Right singular vectors (if return_vecs=True) as rows.

quimb.linalg.base_linalg._SVDS_METHODS
quimb.linalg.base_linalg.svds(A, k, ncv=None, return_vecs=True, backend='AUTO', **kwargs)[source]

Compute the partial singular value decomposition of an operator.

Parameters:
  • A (dense, sparse or linear operator) – The operator to decompose.

  • k (int, optional) – number of singular value (triplets) to retrieve

  • ncv (int, optional) – Number of lanczos vectors to use performing decomposition.

  • return_vecs (bool, optional) – Whether to return the left and right vectors

  • backend ({'AUTO', 'SCIPY', 'SLEPC', 'SLEPC-NOMPI', 'NUMPY'}, optional) – Which solver to use to perform decomposition.

Returns:

Singular value(s) (and vectors) such that Uk @ np.diag(sk) @ VHk approximates A.

Return type:

(Uk,) sk (, VHk)

quimb.linalg.base_linalg.norm_2(A, **kwargs)[source]

Return the 2-norm of operator, A, i.e. the largest singular value.

quimb.linalg.base_linalg.norm_fro_dense(A)[source]

Frobenius norm for dense matrices

quimb.linalg.base_linalg.norm_fro_sparse(A)[source]
quimb.linalg.base_linalg.norm_trace_dense(A, isherm=False)[source]

Returns the trace norm of operator A, that is, the sum of the absolute eigenvalues.

quimb.linalg.base_linalg.norm(A, ntype=2, **kwargs)[source]

Operator norms.

Parameters:
  • A (operator) – The operator to find norm of.

  • ntype (str) –

    Norm to calculate, if any of:

    • {2, ‘2’, ‘spectral’}: largest singular value

    • {‘f’, ‘fro’}: frobenius norm

    • {‘t’, ‘nuc’, ‘tr’, ‘trace’}: sum of singular values

Returns:

x – The operator norm.

Return type:

float

quimb.linalg.base_linalg.expm(A, herm=False)[source]

Matrix exponential, can be accelerated if explicitly hermitian.

Parameters:
  • A (dense or sparse operator) – Operator to exponentiate.

  • herm (bool, optional) – If True (not default), and A is dense, digonalize the matrix in order to perform the exponential.

quimb.linalg.base_linalg._EXPM_MULTIPLY_METHODS
quimb.linalg.base_linalg.expm_multiply(mat, vec, backend='AUTO', **kwargs)[source]

Compute the action of expm(mat) on vec.

Parameters:
  • mat (operator) – Operator with which to act with exponential on vec.

  • vec (vector-like) – Vector to act with exponential of operator on.

  • backend ({'AUTO', 'SCIPY', 'SLEPC', 'SLEPC-KRYLOV', 'SLEPC-EXPOKIT'}) – Which backend to use.

  • kwargs – Supplied to backend function.

Returns:

Result of expm(mat) @ vec.

Return type:

vector

quimb.linalg.base_linalg.sqrtm(A, herm=True)[source]

Matrix square root, can be accelerated if explicitly hermitian.

Parameters:
  • A (dense array) – Operator to take square root of.

  • herm (bool, optional) – If True (the default), and A is dense, digonalize the matrix in order to take the square root.

Return type:

array

class quimb.linalg.base_linalg.IdentityLinearOperator(size, factor=1)[source]

Bases: scipy.sparse.linalg.LinearOperator

Get a LinearOperator representation of the identity operator, scaled by factor.

Parameters:
  • size (int) – The size of the identity.

  • factor (float) – The coefficient of the identity.

Examples

>>> I3 = IdentityLinearOperator(100, 1/3)
>>> p = rand_ket(100)
>>> np.allclose(I3 @ p, p / 3)
True
_matvec(vec)[source]

Default matrix-vector multiplication handler.

If self is a linear operator of shape (M, N), then this method will be called on a shape (N,) or (N, 1) ndarray, and should return a shape (M,) or (M, 1) ndarray.

This default implementation falls back on _matmat, so defining that will define matrix-vector multiplication as well.

_rmatvec(vec)[source]

Default implementation of _rmatvec; defers to adjoint.

_matmat(mat)[source]

Default matrix-matrix multiplication handler.

Falls back on the user-defined _matvec method, so defining that will define matrix multiplication (though in a very suboptimal way).

class quimb.linalg.base_linalg.Lazy(fn, *args, shape=None, factor=None, **kwargs)[source]

A simple class representing an unconstructed matrix. This can be passed to, for example, MPI workers, who can then construct the matrix themselves. The main function fn should ideally take an ownership keyword to avoid forming every row.

This is essentially like using functools.partial and assigning the shape attribute.

Parameters:
  • fn (callable) – A function that constructs an operator.

  • shape – Shape of the constructed operator.

  • args – Supplied to fn.

  • kwargs – Supplied to fn.

Returns:

Lazy

Return type:

callable

Examples

Setup the lazy operator:

>>> H_lazy = Lazy(ham_heis, n=10, shape=(2**10, 2**10), sparse=True)
>>> H_lazy
<Lazy(ham_heis, shape=(1024, 1024), dtype=None)>

Build a matrix slice (usually done automatically by e.g. eigs):

>>> H_lazy(ownership=(256, 512))
<256x1024 sparse matrix of type '<class 'numpy.float64'>'
        with 1664 stored elements in Compressed Sparse Row format>
__imul__(x)[source]
__mul__(x)[source]
__rmul__(x)[source]
__call__(**kwargs)[source]
__repr__()[source]

Return repr(self).