schema_version: 1
title: Complex-Corrected Stochastic Gradient Descent for Noisy Observations
abstract: For entire input scores under exponential derivative envelopes, amplitude
  control and suitable sampling moments, an imaginary Gaussian perturbation yields
  an unbiased clean-gradient oracle. The manuscript derives moment bounds, convergence
  rates and last-iterate and averaged CLTs, with a distinct averaging covariance at
  the inverse-time endpoint.
field: Optimization
license: CC-BY-4.0
type: paper
authors:
- eggtea
keywords:
- SGD
- measurement error
- complex correction
main: 1_paper.pdf
takeaways: takeaways.md
verification:
- type: review
  path: verification/review.md
  status: materials-present-not-independently-verified
  model: GPT-6 (Codex; exact runtime variant not exposed)
  date: '2026-10-17'
  scope: Statement, assumptions, public-summary and supplied-review provenance audit;
    not a complete fresh proof review.
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