Paper Citation Record · LEDGER

Gradient Methods with Online Scaling Part I. Theoretical Foundations

As of 9 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 2 inbound Pith citation observations for arXiv:2505.23081.

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

pith.paper-citation-record.v1
2505.23081 v2

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:10:18.346914Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:35:24.103094Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T18:46:45.040439Z

Reference resolution

72 of 72 outbound references displayed

verified exact1
verified fuzzy49
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 582a86a0-fd6c-4ea8-b92a-5d0679058bde · outbound

This paper cites Disentangling Adaptive Gradient Methods from Learning Rates.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Disentangling Adaptive Gradient Methods from Learning Rates

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:06.894473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 211f7c72-ff18-4c09-98d4-96bdf3d248f3 · outbound

This paper cites Parameter adaptation in stochastic optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Parameter adaptation in stochastic optimization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:33.100819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 599fe075-96bd-486d-81b3-a6cdd78f65b6 · outbound

This paper cites Acceleration by stepsize hedging: Silver stepsize schedule for smooth convex optimization.Mathematical Programming, pages 1–14, 2024.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Acceleration by stepsize hedging: Silver stepsize schedule for smooth convex optimization.Mathematical Programming, pages 1–14, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:32.891809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97a905fd-c4f7-41c4-8a17-67139bb6e03d · outbound

This paper cites Acceleration by stepsize hedging: Multi-step descent and the silver stepsize schedule.Journal of the ACM, 72(2):1–38, 2025.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Acceleration by stepsize hedging: Multi-step descent and the silver stepsize schedule.Journal of the ACM, 72(2):1–38, 2025

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:32.749701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c5b313fa-e5df-4908-9b14-6441dfa666ba · outbound

This paper cites Practical large-scale linear programming using primal-dual hybrid gradient.Advances in Neural Information Processing Systems, 34:20243–20257, 2021.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Practical large-scale linear programming using primal-dual hybrid gradient.Advances in Neural Information Processing Systems, 34:20243–20257, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:32.470056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 685e8275-53dd-47c0-af85-624b89fa7cc4 · outbound

This paper cites Two-point step size gradient methods.IMA journal of numerical analysis, 8(1):141–148, 1988.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Two-point step size gradient methods.IMA journal of numerical analysis, 8(1):141–148, 1988

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:32.237528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e00dd785-aae5-44e8-8e37-adb003f682b6 · outbound

This paper cites Online learning rate adaptation with hypergradient descent.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Online learning rate adaptation with hypergradient descent

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:31.987009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0b9c6f3-bd1d-4b8a-9d02-fa5d750a7e55 · outbound

This paper cites Gradient descent: The ultimate optimizer.Advances in Neural Information Processing Systems, 35:8214–8225, 2022.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Gradient descent: The ultimate optimizer.Advances in Neural Information Processing Systems, 35:8214–8225, 2022

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:31.773172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 08be7905-0102-40c6-a315-5309d64fbfbe · outbound

This paper cites Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:07.974495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc825b92-a85e-441e-ac2f-0b5ca3016d0c · outbound

This paper cites Non-monotonebehavioroftheheavyballmethod.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Non-monotonebehavioroftheheavyballmethod

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:31.467420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 698dab72-3f8c-4753-9886-28607abc2b74 · outbound

This paper cites An enhanced alternating direction method of multipliers-based interior point method for linear and conic optimization.INFORMS Journal on Computing, 2024.

Gradient Methods with Online Scaling Part I. Theoretical Foundations An enhanced alternating direction method of multipliers-based interior point method for linear and conic optimization.INFORMS Journal on Computing, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:31.295597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ad823aab-24c0-474f-b3ef-d0b9e2921766 · outbound

This paper cites Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:08.488432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 700875c0-508d-452e-81d6-ddfa3eec3544 · outbound

This paper cites Adaptive subgradient methods for online learning and stochas- tic optimization.Journal of machine learning research, 12(7), 2011.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive subgradient methods for online learning and stochas- tic optimization.Journal of machine learning research, 12(7), 2011

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:31.076594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 36c42a4e-7125-48b8-9783-19a765fb6ca4 · outbound

This paper cites John Wiley & Sons, 2000.

Gradient Methods with Online Scaling Part I. Theoretical Foundations John Wiley & Sons, 2000

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:30.834841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ea42a4d-5fdf-4220-b05e-a03902fc660f · outbound

This paper cites Gradient Methods with Online Scaling.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Gradient Methods with Online Scaling

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:09.008540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3cc866c-4d62-4b8a-b00e-b6e99c6b6d3f · outbound

This paper cites Scalable Approximate Optimal Diagonal Preconditioning.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Scalable Approximate Optimal Diagonal Preconditioning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:09.165212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7242c41-90e9-4302-98c0-6a9148e4e5ac · outbound

This paper cites Clarabel: An interior-point solver for conic programs with quadratic objectives.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Clarabel: An interior-point solver for conic programs with quadratic objectives

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:09.347627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e87645-c7c9-4b16-a7d6-cb95dde4dbfd · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Shampoo: Preconditioned stochastic tensor optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:30.552063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e82d314b-437f-4135-a23d-8f672afdbcf5 · outbound

This paper cites Introduction to online convex optimization.Foundations and Trends®in Optimization, 2(3-4):157–325, 2016.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Introduction to online convex optimization.Foundations and Trends®in Optimization, 2(3-4):157–325, 2016

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:30.256654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 830e8eff-9523-47ee-986c-3c8d79cc32c5 · outbound

This paper cites Revisiting the Polyak step size.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Revisiting the Polyak step size

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:09.962195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da8727a2-61f7-4d95-a314-16abdb8e8882 · outbound

This paper cites Adaptive online gradient descent.Advances in neural information processing systems, 20, 2007.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive online gradient descent.Advances in neural information processing systems, 20, 2007

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:29.949825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c3541b60-4e1e-4b6e-bb89-7d2e4984a221 · outbound

This paper cites Neural networks for machine learning lecture 6a overview of mini-batch gradient descent.Cited on, 14(8):2, 2012.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Neural networks for machine learning lecture 6a overview of mini-batch gradient descent.Cited on, 14(8):2, 2012

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:29.627509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6011afc-fe50-4103-97f7-d1c2d57e712e · outbound

This paper cites Restarted Primal-Dual Hybrid Conjugate Gradient Method for Large-Scale Quadratic Programming.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Restarted Primal-Dual Hybrid Conjugate Gradient Method for Large-Scale Quadratic Programming

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:10.367209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242d1643-d6d3-4f2e-a90a-ef7b5f1ef31c · outbound

This paper cites Increased rates of convergence through learning rate adaptation.Neural networks, 1(4):295–307, 1988.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Increased rates of convergence through learning rate adaptation.Neural networks, 1(4):295–307, 1988

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:29.321487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d1204d1f-fbf2-41b4-ab6e-d79bf1ea9980 · outbound

This paper cites Unconstrained online learning with unbounded losses.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Unconstrained online learning with unbounded losses

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:29.152637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21918e13-066d-48bb-9e7c-8b7f63fc7b0b · outbound

This paper cites Online learning guided curvature approximation: A quasi-newton method with global non-asymptotic superlinear convergence.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Online learning guided curvature approximation: A quasi-newton method with global non-asymptotic superlinear convergence

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:28.917531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e4d11c3a-afaf-4ef0-bdf8-fd298e008b85 · outbound

This paper cites Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:10:19.028351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a392729-b1cc-4389-b592-503af395f9af · outbound

This paper cites Adaptive hierarchical hyper-gradient descent.International Journal of Machine Learning and Cybernetics, 13(12):3785–3805, 2022.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive hierarchical hyper-gradient descent.International Journal of Machine Learning and Cybernetics, 13(12):3785–3805, 2022

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:28.781008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b509697c-f221-4fff-983a-ed592bab0dd8 · outbound

This paper cites Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:11.348183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0306c6a-5ece-4e3e-996c-773dbf121f8e · outbound

This paper cites Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:11.500660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b6fc337-097e-43af-b5ff-a957bed4e696 · outbound

This paper cites Non-asymptotic superlinear convergence of standard quasi-newton methods.Mathematical Programming, 200(1):425–473, 2023.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Non-asymptotic superlinear convergence of standard quasi-newton methods.Mathematical Programming, 200(1):425–473, 2023

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:28.586015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd73d695-ef08-4ce8-833b-5df61ebae305 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adam: A Method for Stochastic Optimization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:11.796601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda76596-1567-42f0-935a-41cdfc408c9f · outbound

This paper cites Searching for optimal per-coordinate step-sizes with multidimensional backtracking.Advances in Neural Information Processing Systems, 36, 2024.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Searching for optimal per-coordinate step-sizes with multidimensional backtracking.Advances in Neural Information Processing Systems, 36, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:28.347020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f51d06b-a0fb-4662-b0ab-143ebea5d09d · outbound

This paper cites Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:12.138443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f05fe23-b761-4118-9756-402baffd1343 · outbound

This paper cites A simple uniformly optimal method without line search for convex optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations A simple uniformly optimal method without line search for convex optimization

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:12.329475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22afb333-41fb-4049-a4ec-f93bc7ff20bc · outbound

This paper cites A second look at exponential and cosine step sizes: Simplicity, adaptivity, and performance.

Gradient Methods with Online Scaling Part I. Theoretical Foundations A second look at exponential and cosine step sizes: Simplicity, adaptivity, and performance

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:28.024072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 58b31885-133b-402d-846b-f502f5e517c4 · outbound

This paper cites An admm-based interior-point method for large-scale linear programming.Optimization Methods and Software, 36(2-3):389–424, 2021.

Gradient Methods with Online Scaling Part I. Theoretical Foundations An admm-based interior-point method for large-scale linear programming.Optimization Methods and Software, 36(2-3):389–424, 2021

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:27.490108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 10ffd99f-c795-4c93-9a25-2bc8a81907fd · outbound

This paper cites Pdcs: A primal-dual large-scale conic programming solver with gpu enhancements.arXiv preprint arXiv:2505.00311, 2025.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Pdcs: A primal-dual large-scale conic programming solver with gpu enhancements.arXiv preprint arXiv:2505.00311, 2025

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:13.017954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13043e44-b86d-454d-b3e1-a3d1cf0a2a7e · outbound

This paper cites cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia.

Gradient Methods with Online Scaling Part I. Theoretical Foundations cuPDLP.jl: A GPU Implementation of Restarted Primal-Dual Hybrid Gradient for Linear Programming in Julia

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:13.221669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86ae7d01-ff31-4cda-8697-9c946a456fef · outbound

This paper cites cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language.

Gradient Methods with Online Scaling Part I. Theoretical Foundations cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:13.373008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cb11ec2-df2b-4407-8d46-3c97fdc6e0e8 · outbound

This paper cites Tuning-freestep-size adaptation.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Tuning-freestep-size adaptation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:27.135518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d537402d-9002-461e-9a01-fd926b69f4ad · outbound

This paper cites Adaptive gradient descent without descent.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive gradient descent without descent

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:26.619015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 690cc81a-7386-4bb0-b398-2d4fd274d894 · outbound

This paper cites Adaptive proximal gradient method for convex optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive proximal gradient method for convex optimization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:26.349459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 405bcadf-473c-4c53-892d-48321954815b · outbound

This paper cites Adaptive Bound Optimization for Online Convex Optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive Bound Optimization for Online Convex Optimization

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:13.932005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eea46ed-ab74-43c5-9a9f-6f1a7e1fb9f5 · outbound

This paper cites an unresolved cited work.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:10:26.037314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cda48efa-20d9-41cb-84ca-4dcc1c763e04 · outbound

This paper cites Linear convergence of first order methods for non-strongly convex optimization.Mathematical Programming, 175:69–107, 2019.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Linear convergence of first order methods for non-strongly convex optimization.Mathematical Programming, 175:69–107, 2019

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:25.725934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a97f9952-f43a-4b26-ab3b-4bad7f9a6d42 · outbound

This paper cites A method for solving the convex programming problem with convergence rate o (1/k2).

Gradient Methods with Online Scaling Part I. Theoretical Foundations A method for solving the convex programming problem with convergence rate o (1/k2)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:25.422538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af122e1a-fd8f-45d9-8581-b8e70602774c · outbound

This paper cites Springer Science & Business Media, 2013.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Springer Science & Business Media, 2013

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:25.146817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b6fd424a-635e-4a24-b603-d5275a7a25a8 · outbound

This paper cites Springer, 1999.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Springer, 1999

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:24.847880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 073b82e1-a03a-4f53-9384-b6868aad5493 · outbound

This paper cites Conic optimization via operator splitting and homogeneous self-dual embedding.Journal of Optimization Theory and Applications, 169:1042–1068,.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Conic optimization via operator splitting and homogeneous self-dual embedding.Journal of Optimization Theory and Applications, 169:1042–1068,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:24.596628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8fad7c9b-8518-43ef-9b02-cd5c18cbbc9c · outbound

This paper cites Online Learning: A Modern Introduction Using Convex Optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Online Learning: A Modern Introduction Using Convex Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:15.037964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11c62399-0f76-4282-9aeb-3c8a61671e7f · outbound

This paper cites Coin betting and parameter-free online learning.Advances in Neural Information Processing Systems, 29, 2016.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Coin betting and parameter-free online learning.Advances in Neural Information Processing Systems, 29, 2016

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:24.353932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a2d47ced-c099-4894-b9ba-99dab4fdbcd4 · outbound

This paper cites MADA: Meta-adaptive optimizers through hyper-gradient descent.

Gradient Methods with Online Scaling Part I. Theoretical Foundations MADA: Meta-adaptive optimizers through hyper-gradient descent

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:23.997951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7d629dba-c740-40a2-b598-0f19e07ecefa · outbound

This paper cites Introduction to optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Introduction to optimization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:23.506836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0473bbe0-f075-45ac-a88c-d90e57b30df4 · outbound

This paper cites Optimal diagonal precondi- tioning.Operations Research, 2024.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Optimal diagonal precondi- tioning.Operations Research, 2024

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:23.187929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e227455b-df14-4f60-8c1d-8ac694052ba0 · outbound

This paper cites Lecture notes on online learning draft, 2009.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Lecture notes on online learning draft, 2009

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:22.888468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation deef3e93-5088-46c0-b9e4-f524ea99ca99 · outbound

This paper cites On the Convergence of Adam and Beyond.

Gradient Methods with Online Scaling Part I. Theoretical Foundations On the Convergence of Adam and Beyond

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:16.063965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07821767-069b-4dad-ada2-b1eb6a819236 · outbound

This paper cites Greedy quasi-newton methods with explicit superlinear conver- gence.SIAM Journal on Optimization, 31(1):785–811, 2021.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Greedy quasi-newton methods with explicit superlinear conver- gence.SIAM Journal on Optimization, 31(1):785–811, 2021

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:22.657859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fcd11103-fce6-4d6e-9177-588f55060dc1 · outbound

This paper cites New results on superlinear convergence of classical quasi-newton methods.Journal of optimization theory and applications, 188:744–769, 2021.

Gradient Methods with Online Scaling Part I. Theoretical Foundations New results on superlinear convergence of classical quasi-newton methods.Journal of optimization theory and applications, 188:744–769, 2021

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:22.492178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50bcfa29-c047-477c-aa49-627546b67153 · outbound

This paper cites Ratesofsuperlinearconvergenceforclassicalquasi-newtonmethods.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Ratesofsuperlinearconvergenceforclassicalquasi-newtonmethods

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:22.203383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 28a70dfa-4977-4735-8bce-83f2423e0fd4 · outbound

This paper cites Convergence analysis of an adaptive method of gradient descent.University of Oxford, Oxford, M.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Convergence analysis of an adaptive method of gradient descent.University of Oxford, Oxford, M

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:21.869002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 86f09eaa-efb1-48da-a833-c69adcb76bb9 · outbound

This paper cites Local gain adaptation in stochastic gradient descent.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Local gain adaptation in stochastic gradient descent

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:21.591671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a17c6f7-b9c3-4b92-853e-850ce34d27e5 · outbound

This paper cites Adapting bias by gradient descent: An incremental version of delta-bar-delta.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adapting bias by gradient descent: An incremental version of delta-bar-delta

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:21.248493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c4be4a74-e36d-4bec-be13-b0d9120ff377 · outbound

This paper cites No-regret dynamics in the fenchel game: A unified framework for algorithmic convex optimization.Mathematical Programming, 205(1):203–268, 2024.

Gradient Methods with Online Scaling Part I. Theoretical Foundations No-regret dynamics in the fenchel game: A unified framework for algorithmic convex optimization.Mathematical Programming, 205(1):203–268, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:20.916902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2b5126e1-b40e-4efb-b46c-ee6866f94cb9 · outbound

This paper cites On the convergence of stochastic gradient descent with bandwidth-based step size.Journal of Machine Learning Research, 24(48):1–49, 2023.

Gradient Methods with Online Scaling Part I. Theoretical Foundations On the convergence of stochastic gradient descent with bandwidth-based step size.Journal of Machine Learning Research, 24(48):1–49, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:20.605338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81a47c91-72de-40b5-8262-7b20494b7c7e · outbound

This paper cites The Role of Level-Set Geometry on the Performance of PDHG for Conic Linear Optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations The Role of Level-Set Geometry on the Performance of PDHG for Conic Linear Optimization

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:17.588000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09a18a02-c2b9-413e-a58c-08ea1777735d · outbound

This paper cites Adaptive powerball stochastic conjugate gradient for large-scale learning.IEEE Transactions on Big Data, 9(6):1598–1606, 2023.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adaptive powerball stochastic conjugate gradient for large-scale learning.IEEE Transactions on Big Data, 9(6):1598–1606, 2023

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:20.317776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ccd1611f-fda8-426f-b2c8-293a42ac63b6 · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adam-mini: Use Fewer Learning Rates To Gain More

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:10:17.855272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acf4447c-13d9-48d8-bdf1-9b7663236653 · outbound

This paper cites Algorithm 778: L-bfgs-b: Fortran sub- routines for large-scale bound-constrained optimization.ACM Transactions on mathematical software (TOMS), 23(4):550–560, 1997.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Algorithm 778: L-bfgs-b: Fortran sub- routines for large-scale bound-constrained optimization.ACM Transactions on mathematical software (TOMS), 23(4):550–560, 1997

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:20.001214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1caf9fe7-598d-494a-a3d9-884a50a16de7 · outbound

This paper cites Adabelief optimizer: Adapting stepsizes by the belief in observed gradients.Advances in neural information processing systems, 33:18795–18806, 2020.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Adabelief optimizer: Adapting stepsizes by the belief in observed gradients.Advances in neural information processing systems, 33:18795–18806, 2020

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:19.729131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8f4b0a7-8547-4732-9036-7ad4c23feadd · outbound

This paper cites Surrogate losses for online learning of stepsizes in stochastic non-convex optimization.

Gradient Methods with Online Scaling Part I. Theoretical Foundations Surrogate losses for online learning of stepsizes in stochastic non-convex optimization

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:19.409231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9400cacb-1509-4471-be06-d6ca5c05c077 · outbound

This paper cites (cited on 17).

Gradient Methods with Online Scaling Part I. Theoretical Foundations (cited on 17)

Reference 6564

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:10:27.789881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation ff55bcce-22c1-47d3-8546-124f443adfee · inbound

Enhanced PDHG for Linear Programming with Online Preconditioning cites this paper.

Enhanced PDHG for Linear Programming with Online Preconditioning Gradient Methods with Online Scaling Part I. Theoretical Foundations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:35:24.103094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b4f173-62f7-41a0-89ae-6cc9a6d201e3 · inbound

Learning to accelerate distributed ADMM using graph neural networks cites this paper.

Learning to accelerate distributed ADMM using graph neural networks Gradient Methods with Online Scaling Part I. Theoretical Foundations

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:46:45.043079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.