Struct Mask 

pub struct Mask<P: PackedField, Data: Deref<Target = [P]> = Box<[P]>> { /* private fields */ }

Libra mask polynomial for ZK MLE-check protocols.

Stores coefficients for a separable polynomial $g(X) = \sum_i g_i(X_i)$ where each $g_i$ is a univariate polynomial of degree $d$.

The coefficients are stored in a FieldBuffer with m_n + m_d variables where:

• m_n = ceil(log2(n)) - log of number of variables
• m_d = ceil(log2(d + 1)) - log of degree + 1

The buffer is conceptually an n × (d+1) matrix padded to 2^m_n × 2^m_d, with random values in the n × (d+1) submatrix and zeros elsewhere.

The type is generic over the buffer storage type Data, allowing it to work with both owned buffers (Box<[P]>) and borrowed slices.

Implementations

impl<F: Field, P: PackedField<Scalar = F>, Data: Deref<Target = [P]>> Mask<P, Data>

pub const fn new( n_vars: usize, degree: usize, buffer: FieldBuffer<P, Data>, ) -> Self

Creates a new mask polynomial from a pre-allocated buffer.

Arguments

• n_vars - Number of variables (n).
• degree - Degree of each univariate polynomial (d).
• buffer - Buffer with log_len = m_n + m_d.

pub const fn n_vars(&self) -> usize

Returns the number of variables.

pub const fn degree(&self) -> usize

Returns the degree of each univariate polynomial.

pub fn get_coeff(&self, var_index: usize, coeff_index: usize) -> F

Gets the coefficient $g_{i,j}$ (coefficient of $X^j$ in $g_i$).

pub fn coeffs_for_var(&self, var_index: usize) -> impl Iterator<Item = F> + '_

Returns the monomial coefficients [g_{i,0}, g_{i,1}, …, g_{i,d}] for variable i.

pub fn evaluate_univariate(&self, var_index: usize, x: F) -> F

Evaluates g_i(x) for a specific variable using Horner’s method.

pub fn evaluate_mle(&self, eval_point: &[F]) -> F

Computes the MLE of the mask polynomial at a point.

For a mask polynomial $g(X) = \sum_i g_i(X_i)$ where each $g_i$ is univariate, the MLE at point $z$ is:

$$ \sum_{v \in {0,1}^n} g(v) \cdot eq(v, z) = \sum_i [(1-z_i) g_i(0) + z_i g_i(1)] $$

This simplification arises because $\sum_{v_j \in {0,1}} eq_1(v_j, z_j) = 1$.

Trait Implementations

impl<P: PackedField, Data: Deref<Target = [P]>> AsRef<FieldBuffer<P, Data>> for Mask<P, Data>

fn as_ref(&self) -> &FieldBuffer<P, Data>

Converts this type into a shared reference of the (usually inferred) input type.