Paper Citation Record · LEDGER

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2410.10800.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.10800 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:20:53.893055Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-23T04:55:24.879897Z

Reference resolution

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verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 226d8fc2-8fb1-4c37-8f4d-ed80246c9b59 · inbound

On quasi-convex smooth optimization problems by a comparison oracle cites this paper.

On quasi-convex smooth optimization problems by a comparison oracle Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T14:09:46.576636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e65d4d06-6939-4437-879b-52de45629bef · inbound

Toward a Unified Theory of Gradient Descent under Generalized Smoothness cites this paper.

Toward a Unified Theory of Gradient Descent under Generalized Smoothness Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T14:47:46.353647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 681a6793-f8c7-4073-9b53-0b96fa48fa00 · inbound

Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under $(L_0,L_1)$-Smoothness cites this paper.

Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under $(L_0,L_1)$-Smoothness Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T06:02:10.702585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d733a44-e50c-4d27-9e5a-192df14aa794 · inbound

DADA: Dual Averaging with Distance Adaptation cites this paper.

DADA: Dual Averaging with Distance Adaptation Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:55:24.882213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3ef9a0b3-b9c6-44ed-9551-a3b901b2d703 · inbound

Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms cites this paper.

Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T00:25:45.234000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c9e6964-da6a-406d-b9b1-4b6315c04c33 · inbound

Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness cites this paper.

Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T04:56:54.578227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eadd520a-8796-426f-a0f6-4d72d9e82931 · inbound

Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs) cites this paper.

Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs) Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:20:53.893055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c40df07-6873-486d-9c4e-09933ada79a3 · inbound

Gradient-Normalized Smoothness for Optimization with Approximate Hessians cites this paper.

Gradient-Normalized Smoothness for Optimization with Approximate Hessians Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:23.768307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a80581cf-2114-4a12-88fb-cf5e581d546b · inbound

Why Do We Need Warm-up? A Theoretical Perspective cites this paper.

Why Do We Need Warm-up? A Theoretical Perspective Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T12:39:01.193581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87e812ba-6da8-4536-8292-d8bf14d87d7f · inbound

Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates cites this paper.

Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T22:20:05.638577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d41bc6a6-fd21-4c29-a971-c4e302ec131c · inbound

Muon Does Not Converge on Convex Lipschitz Functions cites this paper.

Muon Does Not Converge on Convex Lipschitz Functions Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 91

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:41:37.959062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b3ca4ac1-a5b1-40d8-ba1e-acce49d4565f · inbound

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives cites this paper.

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:37:19.247501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1ae0299-2094-48ea-a0e1-9ff951429c5e · inbound

Normalized First-Order Methods for Convex (L0, L1)-Smooth Optimization with Inexact Gradients cites this paper.

Normalized First-Order Methods for Convex (L0, L1)-Smooth Optimization with Inexact Gradients Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-30T15:28:01.814241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.