Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2409.14989.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T00:25:45.086636Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T20:20:34.589270Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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 2bd2e59b-9a04-4295-9045-c72692262dad · 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 Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T00:25:45.086636Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1f71baf-9fff-4f0d-9ee9-5b1bda34b963 · inbound
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness cites this paper.
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:35:29.642173Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68ffc6e6-f75d-41be-948d-d456195b48e9 · inbound
Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness cites this paper.
Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T04:56:54.545328Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72115b05-738f-407a-ae44-a5cd7453e73a · inbound
Why Do We Need Warm-up? A Theoretical Perspective cites this paper.
Why Do We Need Warm-up? A Theoretical Perspective Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T12:38:55.256221Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54a3ba8c-e914-46b8-a265-e9fb59c12825 · inbound
Frank-Wolfe Algorithms for (L0, L1)-smooth functions cites this paper.
Frank-Wolfe Algorithms for (L0, L1)-smooth functions Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T06:20:58.642480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 45d2dbd3-e2bf-4861-a21a-3007e45b8621 · inbound
Frank-Wolfe Algorithms for (L0, L1)-smooth functions cites this paper.
Frank-Wolfe Algorithms for (L0, L1)-smooth functions Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T20:20:34.591793Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1cda66cf-d77e-4585-b2ab-91139169f005 · 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 Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:20:05.519647Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cce0ba99-4083-49dd-8a86-f826b2647637 · inbound
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients cites this paper.
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T15:17:39.271516Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b775a862-dceb-4b67-95f1-2c412ac17a3a · 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 Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T15:28:01.796584Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.