Struct LogisticKernel 

pub struct LogisticKernel { /* private fields */ }

Logistic kernel for fermionic and bosonic analytical continuation

For bosons the spectral function is divided by the regularizer tanh(βω/2).

This kernel implements K(x, y) = exp(-Λy(x + 1)/2)/(1 + exp(-Λy)) where x ∈ [-1, 1] and y ∈ [-1, 1]

Implementations

impl LogisticKernel

pub fn new(lambda: f64) -> Result<LogisticKernel, Error>

Create a new logistic kernel with the given cutoff parameter

Arguments

• lambda - Kernel cutoff Λ = β ωmax, positive and finite. The SVE grid, and the memory it needs, grows with Λ.

Errors

Error::InvalidParameter if lambda is not positive and finite

pub fn lambda(&self) -> f64

Get the cutoff parameter

Trait Implementations

impl AbstractKernel for LogisticKernel

fn compute<T>(&self, x: T, y: T) -> T

where T: CustomNumeric + Copy + Debug,

Compute the kernel value K(x, y) with high precision

fn is_centrosymmetric(&self) -> bool

Check if the kernel is centrosymmetric

impl CentrosymmKernel for LogisticKernel

fn compute_reduced<T>(&self, x: T, y: T, symmetry: SymmetryType) -> T

where T: CustomNumeric + Copy + Debug,

Compute the reduced kernel value

fn lambda(&self) -> f64

Get the cutoff parameter Λ (lambda)

impl Clone for LogisticKernel

fn clone(&self) -> LogisticKernel

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for LogisticKernel

impl Debug for LogisticKernel

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl KernelProperties for LogisticKernel

type SVEHintsType<T: Copy + Debug + Send + Sync + CustomNumeric + 'static> = LogisticSVEHints<T>

Associated type for SVE hints

fn ypower(&self) -> i32

Power with which the y coordinate scales.

fn conv_radius(&self) -> f64

Convergence radius of the Matsubara basis asymptotic model

fn xmax(&self) -> f64

Get the upper bound of the x domain

fn ymax(&self) -> f64

Get the upper bound of the y domain

fn regularizer<S>(&self, beta: f64, omega: f64) -> f64

where S: StatisticsType + 'static,

A regularizer for a bosonic kernel for avoiding a divergence at omega = 0.

fn sve_hints<T>( &self, epsilon: f64, ) -> <LogisticKernel as KernelProperties>::SVEHintsType<T>

where T: Copy + Debug + Send + Sync + CustomNumeric + 'static,

Create SVE hints for this kernel type.