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LogupZerocheckCpu

Struct LogupZerocheckCpu 

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pub struct LogupZerocheckCpu<'a, SC: StarkProtocolConfig> {
Show 13 fields pub alpha_logup: SC::EF, pub beta_pows: Vec<SC::EF>, pub l_skip: usize, pub n_logup: usize, pub n_max: usize, pub omega_skip: SC::F, pub omega_skip_pows: Vec<SC::F>, pub interactions_layout: StackedLayout, pub constraint_degree: usize, pub n_per_trace: Vec<isize>, pub xi: Vec<SC::EF>, pub mat_evals_per_trace: Vec<Vec<ColMajorMatrix<SC::EF>>>, pub sels_per_trace: Vec<ColMajorMatrix<SC::EF>>, /* private fields */
}

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§alpha_logup: SC::EF§beta_pows: Vec<SC::EF>§l_skip: usize§n_logup: usize§n_max: usize§omega_skip: SC::F§omega_skip_pows: Vec<SC::F>§interactions_layout: StackedLayout§constraint_degree: usize

Max constraint degree across constraints and interactions

§n_per_trace: Vec<isize>§xi: Vec<SC::EF>§mat_evals_per_trace: Vec<Vec<ColMajorMatrix<SC::EF>>>§sels_per_trace: Vec<ColMajorMatrix<SC::EF>>

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impl<'a, SC: StarkProtocolConfig> LogupZerocheckCpu<'a, SC>
where SC::F: TwoAdicField, SC::EF: TwoAdicField + ExtensionField<SC::F>, CpuColMajorBackend<SC>: ProverBackend<Val = SC::F, Matrix = ColMajorMatrix<SC::F>>,

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pub fn new( pk: &'a DeviceMultiStarkProvingKey<CpuColMajorBackend<SC>>, ctx: &ProvingContext<CpuColMajorBackend<SC>>, n_logup: usize, interactions_layout: StackedLayout, alpha_logup: SC::EF, beta_logup: SC::EF, ) -> Result<Self, LogupZerocheckError>

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pub fn sumcheck_uni_round0_polys( &mut self, ctx: &ProvingContext<CpuColMajorBackend<SC>>, lambda: SC::EF, ) -> Result<Vec<UnivariatePoly<SC::EF>>, LogupZerocheckError>

Returns the s_0 polynomials in coefficient form. There should be exactly num_airs_present \* 3 polynomials, in the order (s_0)_{p,T}, (s_0)_{q,T}, (s_0)_{zerocheck,T} per trace T. This is computed before sampling batching randomness mu because the result is used to observe the sum claims sum_{p,T}, sum_{q,T}. The s_0 polynomials could be returned in either coefficient or evaluation form, but we return them all in coefficient form for uniformity and debugging since this interpolation is inexpensive.

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pub fn fold_ple_evals( &mut self, ctx: &ProvingContext<CpuColMajorBackend<SC>>, r_0: SC::EF, )

After univariate sumcheck round 0, fold prismalinear evaluations using randomness r_0. Folding could directly mutate inplace the trace matrices in ctx as they will not be needed after this.

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pub fn sumcheck_polys_eval( &mut self, round: usize, r_prev: SC::EF, ) -> Result<Vec<Vec<SC::EF>>, LogupZerocheckError>

Returns length 3 * num_airs_present polynomials, each polynomial either evaluated at 1,...,deg(s') or at 1 if a linear term (terms in front-loaded sumcheck that have reached exhaustion)

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pub fn fold_mle_evals(&mut self, round: usize, r_round: SC::EF)

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pub fn into_column_openings( &mut self, ) -> Result<Vec<Vec<Vec<SC::EF>>>, LogupZerocheckError>

Auto Trait Implementations§

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impl<'a, SC> Freeze for LogupZerocheckCpu<'a, SC>

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impl<'a, SC> RefUnwindSafe for LogupZerocheckCpu<'a, SC>

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impl<'a, SC> Send for LogupZerocheckCpu<'a, SC>

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impl<'a, SC> Sync for LogupZerocheckCpu<'a, SC>

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impl<'a, SC> Unpin for LogupZerocheckCpu<'a, SC>

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impl<'a, SC> UnsafeUnpin for LogupZerocheckCpu<'a, SC>

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impl<'a, SC> UnwindSafe for LogupZerocheckCpu<'a, SC>

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