Paper Citation Record · LEDGER

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees

As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2508.19712.

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

pith.paper-citation-record.v1
2508.19712 v1

Coverage vector

measured 30 of 30 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-05T15:43:57.070924Z

measured 30 of 30 standing notices

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

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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verified fuzzy7
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Outbound references

Observation 1fa648eb-6b85-4559-a735-0b210f7d40d2 · outbound

This paper cites An accelerated second-order method for dis- tributed stochastic optimization.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees An accelerated second-order method for dis- tributed stochastic optimization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:43:57.592969Z

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

Observation 882489be-ed8e-4efc-9eb3-58e13c2d51c9 · outbound

This paper cites (19) Lemma 4 (Hanzely et al.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees (19) Lemma 4 (Hanzely et al

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:43:57.565502Z

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Observation ccf46b10-bb08-41d6-b6ce-bde2bcf47a73 · outbound

This paper cites an unresolved cited work.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:43:57.528062Z

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Observation b91a0155-806b-485c-8d16-ffdf14f4ac9d · outbound

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

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.355863Z

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Observation e3cfc58b-8e3e-4384-bbbd-01f0b4c89dbd · outbound

This paper cites Cubic Regularization is the Key! The First Accelerated Quasi-Newton Method with a Global Convergence Rate of $O(k^{-2})$ for Convex Functions.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Cubic Regularization is the Key! The First Accelerated Quasi-Newton Method with a Global Convergence Rate of $O(k^{-2})$ for Convex Functions

Reference 10

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Observation 5734c7ec-a7f8-4cb5-b7f1-43cc2befb09d · outbound

This paper cites Explicit Second-Order Min-Max Optimization: Practical Algorithms and Complexity Analysis.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Explicit Second-Order Min-Max Optimization: Practical Algorithms and Complexity Analysis

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.318881Z

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Observation 0818d104-be03-4360-98ee-1302b4413160 · outbound

This paper cites Muon is Scalable for LLM Training.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Muon is Scalable for LLM Training

Reference 13

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Observation cc03ac43-95e7-41a7-9178-6240d9ec33cb · outbound

This paper cites URL https://doi.org/10.1080/10556788.2020.1854252.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees URL https://doi.org/10.1080/10556788.2020.1854252

Reference 16

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Observation b6807173-8bb0-4f9f-934e-248eca276f79 · outbound

This paper cites doi: https://doi.org/10.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees doi: https://doi.org/10

Reference 18

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Observation 73ad18b1-7871-4aac-986a-bd32fdaced42 · outbound

This paper cites doi: 10.1007/s10107-019-01405-z.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees doi: 10.1007/s10107-019-01405-z

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T15:43:57.048034Z

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Observation 3c4f9392-d79f-46a1-9f00-188d87fce2d9 · outbound

This paper cites an unresolved cited work.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:43:57.556596Z

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Observation 256fec9d-f337-48cf-9d2d-8cffc1600178 · outbound

This paper cites Algorithm 4 with parameters θ = 1 +α ≥ 1 +αmax, L≥ (1 +α)3/2Lsemi converges with the rate f (xk+1) − f (x∗) ≤ 270(1 +α)3/2LsemiD 3 k2.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Algorithm 4 with parameters θ = 1 +α ≥ 1 +αmax, L≥ (1 +α)3/2Lsemi converges with the rate f (xk+1) − f (x∗) ≤ 270(1 +α)3/2LsemiD 3 k2

Reference 26

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Observation beb0b82e-0840-43b6-ad9b-87bfe1ed32da · outbound

This paper cites Furthermore, by convexity, we get f (x∗) ≥ f (xt+1) +⟨∇f (xt+1), x∗−xt+1⟩ ≥f (xt+1) − ∥∇f (xk+1)∥∗ Bk ∥x∗ − xk+1∥Bk.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Furthermore, by convexity, we get f (x∗) ≥ f (xt+1) +⟨∇f (xt+1), x∗−xt+1⟩ ≥f (xt+1) − ∥∇f (xk+1)∥∗ Bk ∥x∗ − xk+1∥Bk

Reference 27

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Observation faba8a66-8012-4c22-8f39-4e6badfeae3d · outbound

This paper cites an unresolved cited work.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:43:57.517810Z

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Observation af78e3a7-648a-434d-b858-aaa1acda3364 · outbound

This paper cites (81) D Experiments Our code is available at https://anonymous.4open.science/r/ceqn-stepsizes/.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees (81) D Experiments Our code is available at https://anonymous.4open.science/r/ceqn-stepsizes/

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:43:57.507851Z

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Observation 343e4d9b-96f6-47c0-919a-4ff4d0c073e2 · outbound

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

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)

Reference 1697

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Observation 3ff56f88-0d07-4e18-bda8-02f89db439dc · outbound

This paper cites Gradient Regularization of Newton Method with Bregman Distances.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Gradient Regularization of Newton Method with Bregman Distances

Reference 1972

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.471470Z

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Observation eee4ad59-1abc-4558-9daf-6ec7edd85f4e · outbound

This paper cites Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization

Reference 1993

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Observation 8de9c2ed-0679-4176-b683-18700616ae5d · outbound

This paper cites Regularized Newton Method with Global $O(1/k^2)$ Convergence.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Regularized Newton Method with Global $O(1/k^2)$ Convergence

Reference 2007

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.295269Z

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Observation 152dd27b-b173-4d0a-b237-f38d2720698e · outbound

This paper cites Inexact high-order proximal-point methods with auxiliary search procedure.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Inexact high-order proximal-point methods with auxiliary search procedure

Reference 2008

Resolution: verified exact; doi, observed 2026-08-05T15:43:57.109532Z

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Observation 826f2627-e13e-4107-9b04-08b9f712dc3a · outbound

This paper cites Complexity of the Regularized Newton Method.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Complexity of the Regularized Newton Method

Reference 2009

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.143561Z

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Observation 573e5515-b365-4d72-98ee-b95f4e972971 · outbound

This paper cites Research in this area typically addresses two main aspects: local convergence properties and globalization strategies.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Research in this area typically addresses two main aspects: local convergence properties and globalization strategies

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:43:57.574845Z

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Observation 2b52e38d-9a9b-4055-be90-1a73455f09ab · outbound

This paper cites Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence

Reference 2017

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:43:57.156603Z

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Observation 0a0517f9-8b8e-4a36-b0f5-3f9bf73c6d58 · outbound

This paper cites Second-Order Methods with Cubic Regularization Under Inexact Information.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Second-Order Methods with Cubic Regularization Under Inexact Information

Reference 2019

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Observation 24061f92-daee-4486-932f-0c4e14f8a674 · outbound

This paper cites Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.382656Z

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Observation a82fe1f6-5d8d-4e6f-b6e0-6365178ce202 · outbound

This paper cites FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:43:57.498102Z

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Observation 93590fbb-f4f0-49df-9542-98a59aefed8d · outbound

This paper cites doi: https://doi.org/10.1016/j.ejco.2022.100045.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees doi: https://doi.org/10.1016/j.ejco.2022.100045

Reference 2022

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Observation a20ecb4e-7dc5-4801-a7c0-2d0fd1885959 · outbound

This paper cites Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:43:57.484552Z

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Observation eb7e5f61-aec7-4b63-9bc4-0f30dc627b04 · outbound

This paper cites Newton Method Revisited: Global Convergence Rates up to $\mathcal {O}\left(k^{-3} \right)$ for Stepsize Schedules and Linesearch Procedures.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Newton Method Revisited: Global Convergence Rates up to $\mathcal {O}\left(k^{-3} \right)$ for Stepsize Schedules and Linesearch Procedures

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:43:57.368993Z

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Observation 81b7d96b-eb6f-448d-8fd9-32f1a03a4aa3 · outbound

This paper cites Global non-asymptotic super-linear convergence rates of regularized proximal quasi-Newton methods on non-smooth composite problems.

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees Global non-asymptotic super-linear convergence rates of regularized proximal quasi-Newton methods on non-smooth composite problems

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T15:43:57.045262Z

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Pith citing papers

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