Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:10:18.346914Z
Paper Citation Record · LEDGER
Gradient Methods with Online Scaling Part I. Theoretical Foundations
As of 20 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 3 inbound Pith citation observations for arXiv:2505.23081.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:38:41.132738Z
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
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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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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,
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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
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- 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 2026-08-20T06:33:59.587034+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
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- 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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+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
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- 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 2026-08-20T06:33:59.587034+00:00.
Observation a17a2056-3d84-44f7-a89b-1f954def582f · inbound
Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp cites this paper.
Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp Gradient Methods with Online Scaling Part I. Theoretical Foundations
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:38:41.132738Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.