Public names index

AugmentedBasis <: AbstractBasis

Augmented basis on the imaginary-time/frequency axis.

Groups a set of additional functions, augmentations, with a given basis. The augmented functions then form the first basis functions, while the rest is provided by the regular basis, i.e.:

u[l](x) == l < naug ? augmentations[l](x) : basis.u[l-naug](x),

where naug = length(augmentations) is the number of added basis functions through augmentation. Similar expressions hold for Matsubara frequencies.

Augmentation is useful in constructing bases for vertex-like quantities such as self-energies ^{[wallerberger2021]} and when constructing a two-point kernel that serves as a base for multi-point functions ^{[shinaoka2018]}.

See also: MatsubaraConst for vertex basis ^{[wallerberger2021]}, TauConst, TauLinear for multi-point ^{[shinaoka2018]}

Bosonic statistics.

DiscreteLehmannRepresentation(basis::AbstractBasis, poles=nothing)

Construct a DLR basis from an IR basis.

If poles is not provided, uses the default omega sampling points from the IR basis.

DiscreteLehmannRepresentation{S,B} <: AbstractBasis{S}

Discrete Lehmann representation (DLR) with poles selected according to extrema of IR.

This type wraps the C API DLR functionality. The DLR basis is a variant of the IR basis that uses a "sketching" approach - representing functions as a linear combination of poles on the real-frequency axis:

G(iv) == sum(a[i] / (iv - w[i]) for i in 1:npoles)

Fields

• ptr::Ptr{spir_basis}: Pointer to the C DLR object
• basis::B: The underlying IR basis
• poles::Vector{Float64}: Pole locations on the real-frequency axis

Fermionic statistics.

FiniteTempBasis <: AbstractBasis

Intermediate representation (IR) basis for given temperature.

For a continuation kernel K from real frequencies, ω ∈ [-ωmax, ωmax], to imaginary time, τ ∈ [0, β], this type stores the truncated singular value expansion or IR basis:

K(τ, ω) ≈ sum(u[l](τ) * s[l] * v[l](ω) for l in 1:L)

This basis is inferred from a reduced form by appropriate scaling of the variables.

Fields

• u::PiecewiseLegendrePolyVector: Set of IR basis functions on the imaginary time (tau) axis. These functions are stored as piecewise Legendre polynomials.

  To obtain the value of all basis functions at a point or a array of points x, you can call the function u(x). To obtain a single basis function, a slice or a subset l, you can use u[l].

• uhat::PiecewiseLegendreFTVector: Set of IR basis functions on the Matsubara frequency (wn) axis. These objects are stored as a set of Bessel functions.

  To obtain the value of all basis functions at a Matsubara frequency or a array of points wn, you can call the function uhat(wn). Note that we expect reduced frequencies, which are simply even/odd numbers for bosonic/fermionic objects. To obtain a single basis function, a slice or a subset l, you can use uhat[l].

• s: Vector of singular values of the continuation kernel

• v::PiecewiseLegendrePolyVector: Set of IR basis functions on the real frequency (w) axis. These functions are stored as piecewise Legendre polynomials.

  To obtain the value of all basis functions at a point or a array of points w, you can call the function v(w). To obtain a single basis function, a slice or a subset l, you can use v[l].

FiniteTempBasis{S}(β, ωmax, ε; kernel=LogisticKernel(β * ωmax), sve_result=SVEResult(kernel, ε), max_size=-1)

Construct a finite temperature basis suitable for the given S (Fermionic or Bosonic) and cutoffs β and ωmax.

Arguments

• β: Inverse temperature (must be positive)
• ωmax: Frequency cutoff (must be non-negative)
• ε: This parameter controls the number of basis functions. Only singular values ≥ ε * s[1] are kept. Typical values are 1e-6 to 1e-12 depending on the desired accuracy for your calculations. If ε is smaller than the square root of double precision machine epsilon (≈ 1.49e-8), the library will automatically use higher precision for the singular value decomposition, resulting in longer computation time for basis generation.

The number of basis functions grows logarithmically as log(1/ε) log (β * ωmax).

FiniteTempBasis(stat::Statistics, β, ωmax, ε; kernel=LogisticKernel(β * ωmax), sve_result=SVEResult(kernel, ε), max_size=-1)

Convenience constructor that matches SparseIR.jl signature.

Construct a finite temperature basis for the given statistics type and cutoffs.

Arguments

• stat: Statistics type (Fermionic() or Bosonic())
• β: Inverse temperature (must be positive)
• ωmax: Frequency cutoff (must be non-negative)
• ε: Accuracy target for the basis. This parameter controls the number of basis functions. Only singular values ≥ ε * s[1] are kept. Typical values are 1e-6 to 1e-12 depending on the desired accuracy for your calculations. If ε is smaller than the square root of double precision machine epsilon (≈ 1.49e-8), the library will automatically use higher precision for the singular value decomposition, resulting in longer computation time for basis generation.

The number of basis functions grows logarithmically as log(1/ε) log (β * ωmax).

FiniteTempBasisSet

Type for holding IR bases and sparse-sampling objects.

An object of this type holds IR bases for fermions and bosons and associated sparse-sampling objects.

Fields

• basis_f::FiniteTempBasis: Fermion basis
• basis_b::FiniteTempBasis: Boson basis
• tau::Vector{Float64}: Sampling points in the imaginary-time domain
• wn_f::Vector{Int}: Sampling fermionic frequencies
• wn_b::Vector{Int}: Sampling bosonic frequencies
• smpltauf::TauSampling: Sparse sampling for tau & fermion
• smpltaub::TauSampling: Sparse sampling for tau & boson
• smplwnf::MatsubaraSampling: Sparse sampling for Matsubara frequency & fermion
• smplwnb::MatsubaraSampling: Sparse sampling for Matsubara frequency & boson
• sve_result::Tuple{PiecewiseLegendrePoly,Vector{Float64},PiecewiseLegendrePoly}: Results of SVE

Getters

• beta::Float64: Inverse temperature
• ωmax::Float64: Cut-off frequency

LogisticKernel <: AbstractKernel

Fermionic/bosonic analytical continuation kernel.

In dimensionless variables $x = 2 τ/β - 1$, $y = β ω/Λ$, the integral kernel is a function on $[-1, 1] × [-1, 1]$:

\[ K(x, y) = \frac{e^{-Λ y (x + 1) / 2}}{1 + e^{-Λ y}}\]

LogisticKernel is a fermionic analytic continuation kernel. Nevertheless, one can model the $τ$ dependence of a bosonic correlation function as follows:

\[ ∫ \frac{e^{-Λ y (x + 1) / 2}}{1 - e^{-Λ y}} ρ(y) dy = ∫ K(x, y) ρ'(y) dy,\]

with

\[ ρ'(y) = w(y) ρ(y),\]

where the weight function is given by

\[ w(y) = \frac{1}{\tanh(Λ y/2)}.\]

MatsubaraConst <: AbstractAugmentation

Constant in Matsubara, undefined in imaginary time.

MatsubaraFreq(n)

Prefactor n of the Matsubara frequency ω = n*π/β

Struct representing the Matsubara frequency ω entering the Fourier transform of a propagator G(τ) on imaginary time τ to its Matsubara equivalent Ĝ(iω) on the imaginary-frequency axis:

        β
Ĝ(iω) = ∫  dτ exp(iωτ) G(τ)      with    ω = n π/β,
        0

where β is inverse temperature and by convention we include the imaginary unit in the frequency argument, i.e, Ĝ(iω). The frequencies depend on the statistics of the propagator, i.e., we have that:

G(τ - β) = ± G(τ)

where + is for bosons and - is for fermions. The frequencies are restricted accordingly.

• Bosonic frequency (S == Fermionic): n even (periodic in β)
• Fermionic frequency (S == Bosonic): n odd (anti-periodic in β)

MatsubaraSampling{T,B} <: AbstractSampling

Sparse sampling in Matsubara frequencies using the C API.

Allows transformation between IR basis coefficients and sampling points in Matsubara frequencies.

MatsubaraSampling(basis::AbstractBasis; positive_only=false, sampling_points=nothing, factorize=true)

Construct a MatsubaraSampling object from a basis. If sampling_points is not provided, the default Matsubara sampling points from the basis are used.

If positive_only=true, assumes functions are symmetric in Matsubara frequency.

RegularizedBoseKernel <: AbstractKernel

Regularized bosonic analytical continuation kernel.

In dimensionless variables $x = 2 τ/β - 1$, $y = β ω/Λ$, the fermionic integral kernel is a function on $[-1, 1] × [-1, 1]$:

\[ K(x, y) = y \frac{e^{-Λ y (x + 1) / 2}}{e^{-Λ y} - 1}\]

Care has to be taken in evaluating this expression around $y = 0$.

TauConst <: AbstractAugmentation

Constant in imaginary time/discrete delta in frequency.

TauLinear <: AbstractAugmentation

Linear function in imaginary time, antisymmetric around β/2.

TauSampling{T,B} <: AbstractSampling

Sparse sampling in imaginary time using the C API.

Allows transformation between IR basis coefficients and sampling points in imaginary time.

TauSampling(basis::AbstractBasis; sampling_points=nothing, use_positive_taus=true)

Construct a TauSampling object from a basis. If sampling_points is not provided, the default tau sampling points from the basis are used.

If use_positive_taus=true, the sampling points are folded to the positive tau domain [0, β) [default].

If use_positive_taus=false, the sampling points are in the range [-β/2, β/2].

default_omega_sampling_points(basis::AbstractBasis)

Get the default real-frequency sampling points for a basis.

These are the extrema of the highest-order basis function on the real-frequency axis, which provide near-optimal conditioning for the DLR.

evaluate!(output::Array, sampling::AbstractSampling, al::Array; dim=1)

In-place version of evaluate. Write results to the pre-allocated output array.

evaluate(sampling::AbstractSampling, al::Array; dim=1)

Evaluate basis coefficients at the sampling points using the C API.

For multidimensional arrays, dim specifies which dimension corresponds to the basis coefficients.

fit!(output::Array, sampling::AbstractSampling, al::Array; dim=1)

In-place version of fit. Write results to the pre-allocated output array.

fit(sampling::AbstractSampling, al::Array; dim=1)

Fit basis coefficients from values at sampling points using the C API.

For multidimensional arrays, dim specifies which dimension corresponds to the sampling points.

from_IR(dlr::DiscreteLehmannRepresentation, gl::Array, dims=1)

Transform from IR basis coefficients to DLR coefficients.

Arguments

• dlr: The DLR basis
• gl: IR basis coefficients
• dims: Dimension along which the basis coefficients are stored

Returns

DLR coefficients with the same shape as input, but with size length(dlr) along dimension dims.

get_poles(dlr::DiscreteLehmannRepresentation)

Get the pole locations for the DLR basis.

Returns a vector of pole locations on the real-frequency axis.

npoints(sampling::AbstractSampling)

Get the number of sampling points.

npoles(dlr::DiscreteLehmannRepresentation)

Get the number of poles in the DLR basis.

overlap(poly::PiecewiseLegendrePoly, f, xmin::Float64, xmax::Float64; 
    rtol=eps(), return_error=false, maxevals=10^4, points=Float64[])

Evaluate overlap integral of poly with arbitrary function f.

Given the function f, evaluate the integral

∫ dx f(x) poly(x)

using adaptive Gauss-Legendre quadrature.

points is a sequence of break points in the integration interval where local difficulties of the integrand may occur (e.g. singularities, discontinuities).

overlap(poly::PiecewiseLegendrePoly, f; 
    rtol=eps(), return_error=false, maxevals=10^4, points=Float64[])

Evaluate overlap integral of poly with arbitrary function f using default range.

Given the function f, evaluate the integral

∫ dx f(x) poly(x)

using adaptive Gauss-Legendre quadrature with the default integration range.

points is a sequence of break points in the integration interval where local difficulties of the integrand may occur (e.g. singularities, discontinuities).

overlap(polys::PiecewiseLegendrePolyVector, f; 
    rtol=eps(), return_error=false, maxevals=10^4, points=Float64[])

Evaluate overlap integral of polys with arbitrary function f using default range.

Given the function f, evaluate the integral

∫ dx f(x) polys[i](x)

for each polynomial in the vector using adaptive Gauss-Legendre quadrature with the default integration range.

sampling_points(sampling::AbstractSampling)

Return sampling points.

to_IR(dlr::DiscreteLehmannRepresentation, g_dlr::Array, dims=1)

Transform from DLR coefficients to IR basis coefficients.

Arguments

• dlr: The DLR basis
• g_dlr: DLR coefficients
• dims: Dimension along which the DLR coefficients are stored

Returns

IR basis coefficients with the same shape as input, but with size length(dlr.basis) along dimension dims.