Transient objective convergence of spectrally weighted gradient descent

AI-assisted, unrefereed candidate on ordinary-objective transient convergence for spectrally weighted single-gradient-direction methods. With smallest eigenvalue one, positive nondecreasing unbounded weights, fixed horizon K and relaxation 0<omega<=1, the worst-case ratio has leading deficit 2 omega K/kappa. Power weights admit sublogarithmic sharpness and a logarithmic interval of simultaneous failures of a strong two-step interpretation. The weaker interpretation remains true; no proved source theorem is refuted.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-06
DOI
https://doi.org/10.5281/zenodo.24001733
Primary Topic
Stochastic Gradient Optimization Techniques
Type
preprint
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preprint

Transient objective convergence of spectrally weighted gradient descent

Zenodo (CERN European Organization for Nuclear Research)
Stochastic Gradient Optimization Techniques
preprint

Transient objective convergence of spectrally weighted gradient descent

preprint en

Abstract

AI-assisted, unrefereed candidate on ordinary-objective transient convergence for spectrally weighted single-gradient-direction methods. With smallest eigenvalue one, positive nondecreasing unbounded weights, fixed horizon K and relaxation 0<omega<=1, the worst-case ratio has leading deficit 2 omega K/kappa. Power weights admit sublogarithmic sharpness and a logarithmic interval of simultaneous failures of a strong two-step interpretation. The weaker interpretation remains true; no proved source theorem is refuted.

Zenodo (CERN European Organization for Nuclear Research)
Stochastic Gradient Optimization Techniques
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Transient objective convergence of spectrally weighted gradient descent · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS