pith:UDGTCHTA
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
Complex SGD converges under the same assumptions as real SGD and extends directional bias to kernel regression in complex RKHS.
arxiv:2604.23017 v2 · 2026-04-24 · cs.LG · cs.NA · math.CV · math.NA
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Claims
We propose a variant of SGD (complex SGD) that allows for complex parameters, and we provide convergence guarantees under assumptions that parallel those from the real setting. Notably, these results extend to GD as well, and with the same set of assumptions, we confirm that some directional bias results extend from the real to the complex setting for kernel regression problems.
The assumptions that parallel those from the real setting (such as convexity, smoothness, or bounded variance) remain valid and sufficient when parameters and gradients are complex-valued, without additional analyticity requirements.
Complex SGD converges without analyticity constraints and extends real-valued directional bias results to complex RKHS, with demonstrations on Fock and Hardy spaces.
Receipt and verification
| First computed | 2026-05-26T02:04:11.318890Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a0cd311e6058759ce74935c0860e8a4eb90604f63c9a83932c45f30f55563801
Aliases
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/UDGTCHTALB2ZZZ2JGXAIMDUKJ2 \
| jq -c '.canonical_record' \
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Canonical record JSON
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