Paper Citation Record · LEDGER

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions

As of 16 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.03222.

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

pith.paper-citation-record.v1
2505.03222 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:07:41.894418Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

23 of 23 outbound references displayed

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verified fuzzy16
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External citation measurements

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Outbound references

Observation 073db737-bd19-4aed-a78f-a34f4cfa0527 · outbound

This paper cites On exponential convergence of SGD in non-convex over-parametrized learning.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions On exponential convergence of SGD in non-convex over-parametrized learning

Reference 1

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Source-reported events for the cited work

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Observation 3424f3c6-bf3c-492d-be29-f22b3a4af1f4 · outbound

This paper cites Large-scale machine learning with stochastic gradient descent.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Large-scale machine learning with stochastic gradient descent

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.291069Z

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Observation 90034790-a1ff-4e57-96fa-c61d36ea1bbd · outbound

This paper cites Diffusion for global optimization in Rn.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Diffusion for global optimization in Rn

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.273728Z

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Observation 94d32fed-e136-4479-afb1-414be8349355 · outbound

This paper cites Large Deviations Techniques and Applications.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Large Deviations Techniques and Applications

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.257954Z

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Observation be787f06-8f7f-4554-be59-de0d3cc0f291 · outbound

This paper cites An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions An Algebraically Converging Stochastic Gradient Descent Algorithm for Global Optimization

Reference 5

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Source-reported events for the cited work

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Observation 2607c26d-d29d-45f4-b1ce-e02782f48422 · outbound

This paper cites Adaptive state-dependent diffusion for derivative-free optimization.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Adaptive state-dependent diffusion for derivative-free optimization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.242638Z

Source-reported events for the cited work

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Observation 9b6f47ca-27ba-4832-8324-59c58ab9bf55 · outbound

This paper cites Escaping from saddle points — online stochastic gradient for tensor decomposition.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Escaping from saddle points — online stochastic gradient for tensor decomposition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.227313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8a36b9f4-6ac5-4727-9aa0-14b7745f3a08 · outbound

This paper cites Recursive stochastic algorithms for global optimization in Rd.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Recursive stochastic algorithms for global optimization in Rd

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.211188Z

Source-reported events for the cited work

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Observation 07785418-4fd6-48f0-af34-8cf29999a109 · outbound

This paper cites Diffusions for global optimization.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Diffusions for global optimization

Reference 9

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Source-reported events for the cited work

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Observation 3b260996-e200-43da-ba61-0e3714040455 · outbound

This paper cites Gradient descent is optimal under lower restricted secant inequality and upper error bound.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Gradient descent is optimal under lower restricted secant inequality and upper error bound

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.185026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5632c56d-be87-4c82-a82e-9ac0e8962c1e · outbound

This paper cites Large-time behavior of perturbed diffusion markov pro- cesses with applications to the second eigenvalue problem for fokker-planck operators and simulated annealing.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Large-time behavior of perturbed diffusion markov pro- cesses with applications to the second eigenvalue problem for fokker-planck operators and simulated annealing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.167859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation efeb3e14-d6a6-4bb1-807b-b18a2619d78b · outbound

This paper cites Kakade, and Michael I.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Kakade, and Michael I

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.148603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a7123591-272f-4a71-938e-5d5628a4f157 · outbound

This paper cites Linear convergence of gradient and proximal- gradient methods under the polyak-/suppress lojasiewicz condition.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Linear convergence of gradient and proximal- gradient methods under the polyak-/suppress lojasiewicz condition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.130661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8cfde17-fbf5-44df-88f5-468938bc14fd · outbound

This paper cites an unresolved cited work.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:07:42.113008Z

Source-reported events for the cited work

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Observation 6899d11f-6967-4c2a-9f1a-b09480b1096c · outbound

This paper cites Asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects: global minimization via monte carlo.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects: global minimization via monte carlo

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.096313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 10212a4c-708d-4afe-a932-80044a62df70 · outbound

This paper cites Error bounds, PL condition, and quadratic growth for weakly convex functions, and linear convergences of proximal point methods.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Error bounds, PL condition, and quadratic growth for weakly convex functions, and linear convergences of proximal point methods

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.079551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 263e51fe-eeb6-49ad-8961-53f46a61cf9a · outbound

This paper cites SGDR: Stochastic gradient descent with warm restarts.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions SGDR: Stochastic gradient descent with warm restarts

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T00:07:41.863592Z

Source-reported events for the cited work

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Observation 68cb8f6b-8a9d-42d9-addc-863c69f85778 · outbound

This paper cites Linear convergence of first order methods for non-strongly convex optimization.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Linear convergence of first order methods for non-strongly convex optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.052868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5dc3c59e-abd3-4c76-960b-694bc8fab350 · outbound

This paper cites Gradient methods for the minimisation of functionals.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Gradient methods for the minimisation of functionals

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.034954Z

Source-reported events for the cited work

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Observation 51e3f478-fcde-46cb-9b80-1e66e6255cd1 · outbound

This paper cites Systems of extremal control.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Systems of extremal control

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:42.017529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9b5d14b6-f0e5-4727-bbda-921f901e171d · outbound

This paper cites Stochastic convex optimization.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Stochastic convex optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:41.999819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96f8e17c-1694-4974-88b7-58127b83d702 · outbound

This paper cites How Does Learning Rate Decay Help Modern Neural Networks?.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions How Does Learning Rate Decay Help Modern Neural Networks?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:07:41.889133Z

Source-reported events for the cited work

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Observation d281af61-3670-485f-8d33-5eaf11f39ad8 · outbound

This paper cites Gradient methods for convex minimization: better rates under weaker conditions.

A Stochastic Gradient Descent Method for Globally Minimizing Nearly Convex Functions Gradient methods for convex minimization: better rates under weaker conditions

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T00:07:41.894418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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