Paper Citation Record · LEDGER

The Stochastic Multi-Proximal Method for Nonsmooth Optimization

As of 23 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2505.12409.

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

pith.paper-citation-record.v1
2505.12409 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:57.034203Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:12:47.513382Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T07:20:59.746446Z

Reference resolution

26 of 26 outbound references displayed

verified exact4
verified fuzzy9
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40fce754-9db8-43e2-b7c1-d657b6e8cd37 · outbound

This paper cites Optimal Gradient Compression for Distributed and Federated Learning.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Optimal Gradient Compression for Distributed and Federated Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.930305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14025093-402e-4d22-bfca-c20462c0e79c · outbound

This paper cites Improving Accelerated Federated Learning with Compression and Importance Sampling.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Improving Accelerated Federated Learning with Compression and Importance Sampling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.961994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 006cfd0a-f143-43b5-b7b3-9870133a93ae · outbound

This paper cites Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:57.171987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3697669c-3a0a-432f-930b-8afefdbc7bc6 · outbound

This paper cites Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.974655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 197208f8-2a9f-4d2e-89ea-5e912a1fc6e0 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.979135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93dcd450-89b3-4ddf-9e4a-4a7cabb36874 · outbound

This paper cites Distributed Learning with Compressed Gradient Differences.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Distributed Learning with Compressed Gradient Differences

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.987520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c90ebdb-025a-45c7-ad67-4af192206d6d · outbound

This paper cites Reisizadeh, A.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Reisizadeh, A

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.547831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 68e3bd03-528f-453d-b009-ec109df7c30f · outbound

This paper cites Traoré, V.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Traoré, V

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.535015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18c00f18-5529-4e8c-8ca5-c0cc2117fa7c · outbound

This paper cites A Field Guide to Federated Optimization.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization A Field Guide to Federated Optimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:57.003281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c9d2180-b413-4281-ac22-5a6a2509af30 · outbound

This paper cites Xiao and T.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Xiao and T

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.519396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d220766-d846-4973-be50-7a2daa276954 · outbound

This paper cites Almost sure convergence ofxt and theut i follows.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Almost sure convergence ofxt and theut i follows

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.492482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 82f3438d-68e2-40e1-9a81-a75fa0e9a2d8 · outbound

This paper cites This is better than uniform sampling withp1 =··· =pn = 1 n as in Corollary C.3 withs= 1, since the complexity now depends on¯Lh instead ofmaxiLhi.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization This is better than uniform sampling withp1 =··· =pn = 1 n as in Corollary C.3 withs= 1, since the complexity now depends on¯Lh instead ofmaxiLhi

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.479809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 21d8dc70-08f5-4b83-94f5-08177252e7e7 · outbound

This paper cites InSMPM, suppose thatγt≡γ for some0 <γ < 2 Lf (or justγ >0if f = 0), ˆp= 1−p∅ 1−p∅+γµh1 and η1 = 1 1−p∅.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization InSMPM, suppose thatγt≡γ for some0 <γ < 2 Lf (or justγ >0if f = 0), ˆp= 1−p∅ 1−p∅+γµh1 and η1 = 1 1−p∅

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.467345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a8ed61f7-591f-45f2-8bd4-17a139c34118 · outbound

This paper cites Almost sure convergence ofxt and theut i follows.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Almost sure convergence ofxt and theut i follows

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.454162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d32c0395-8ca7-4599-9c66-9bb6f0984358 · outbound

This paper cites We can compare our linear rates to known rates in the literature, keeping in mind that they are not for the same Lyapunov function.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization We can compare our linear rates to known rates in the literature, keeping in mind that they are not for the same Lyapunov function

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.439373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ddfdbc6a-65b3-412a-8eee-499d4416e6ef · outbound

This paper cites an unresolved cited work.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Unresolved cited work

Reference 2013

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:57.506445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fd500523-33bb-45cb-8e33-47e1c1ea247a · outbound

This paper cites Convergence Analyses of Davis-Yin Splitting via Scaled Relative Graphs II: Convex Optimization Problems.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Convergence Analyses of Davis-Yin Splitting via Scaled Relative Graphs II: Convex Optimization Problems

Reference 2014

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:57.072984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a8aff9a0-40f8-4ad6-ae27-57d2cccd30eb · outbound

This paper cites On Biased Compression for Distributed Learning.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization On Biased Compression for Distributed Learning

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.935157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39136755-0f40-4261-b61e-ef4ed62912f9 · outbound

This paper cites Stochastic Proximal Point Methods for Monotone Inclusions under Expected Similarity.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Stochastic Proximal Point Methods for Monotone Inclusions under Expected Similarity

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.995757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d60af7-59fa-4edb-a770-8b5975eeca54 · outbound

This paper cites A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:57.131974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b4286fc-1b60-4806-9346-24634f17cae0 · outbound

This paper cites EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.953883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7682077-c2d5-4dfe-8654-a0ef1b0fd2f0 · outbound

This paper cites an unresolved cited work.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:57.559726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 37b71a0b-6532-4845-8513-41858627f624 · outbound

This paper cites Bonawitz, V.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Bonawitz, V

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:57.571893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eafd06e4-a3e9-4a87-aabc-6a8c221b3143 · outbound

This paper cites One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.966491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0c60f1e-b00d-4e79-b7d4-1a660c338add · outbound

This paper cites A Simple Linear Convergence Analysis of the Point-SAGA Algorithm.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization A Simple Linear Convergence Analysis of the Point-SAGA Algorithm

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:57.395912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6195136a-568b-4d1c-84d0-6ebf7ec8f98d · outbound

This paper cites Condat, I.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Condat, I

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.949604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c54fc86e-c056-4641-a949-f2149f12d2ad · inbound

A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization cites this paper.

A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization The Stochastic Multi-Proximal Method for Nonsmooth Optimization

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:20:59.756992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.