Struct Domain 

pub struct Domain<F>
where
    F: FftField,
{
    pub domains: Domains<F>,
    pub zk_rows: usize,
    pub capacity: usize,
    pub not_last_row: FieldColumn<F>,
    pub l_first: FieldColumn<F>,
    pub l_last: FieldColumn<F>,
    /* private fields */
}

Fields

domains: Domains<F>

zk_rows: usize

capacity: usize

not_last_row: FieldColumn<F>

l_first: FieldColumn<F>

l_last: FieldColumn<F>

Implementations

impl<F> Domain<F>

where F: FftField,

pub fn no_zk(n: usize) -> Domain<F>

Returns a “non-blinding” domain of full capacity = N.

pub fn with_zk_rows(n: usize, zk_rows: usize) -> Domain<F>

Returns a domain that blinds (column) polynomials. The highest zk_rows evaluations (aka Lagrange coefficients) are set random. After interpolation that is equivallent to adding r(X).Z'(X), for a random deg(r) = zk_rows - 1.

pub fn without_blinding(self) -> Domain<F>

Disables column blinding, preserving the domain layout (zk_rows, capacity): the zk rows are padded with zeros instead of random values. Proofs generated over such a domain are deterministic, thus NOT zero-knowledge, but remain valid for verifiers configured with the blinding-enabled domain. Intended for reproducible test vectors generation.

pub fn is_hiding(&self) -> bool

pub fn compute_quotient( &self, poly: &DensePolynomial<F>, ) -> Option<DensePolynomial<F>>

pub fn column(&self, values: Vec<F>) -> FieldColumn<F>

pub fn public_column(&self, values: Vec<F>) -> FieldColumn<F>

pub fn domain(&self) -> GeneralEvaluationDomain<F>

pub fn domain_size(&self) -> usize

pub fn omega(&self) -> F

pub fn evaluate(&self, zeta: F) -> EvaluatedDomain<F>

Trait Implementations

impl<F> Clone for Domain<F>

where F: Clone + FftField,

fn clone(&self) -> Domain<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.