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LWE

Learning With Errors

Recover or distinguish a noisy linear system.

LWE · custom regime

Specify dimension, modulus, error, secret, and samples

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ClassicalRegime dependentLWE distribution2c n + o(n)
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#PUBLICATION / AUTHORSDATETIME BOUNDSPACE BOUNDEVIDENCE
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0 of 0 entriesIncludes relevant non-record results. Dates refer to the specific bound.
Comparison definition & assumptions
Regime
Specify dimension, modulus, error, secret, and samples
Computation
Classical
Rigor
Regime dependent
Instances
LWE distribution
Cost scale
2^{c n + o(n)}
Parameter
n
Description
A template for comparing primal, dual, hybrid, algebraic, or combinatorial algorithms in one fixed LWE regime.
Comparison rules
Fix q(n), error and secret distributions, sample access, and search/decision objective. Record the attack model and any SVP-oracle heuristic. Different parameter regimes require different categories.
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