Paper Citation Record · LEDGER

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.01092.

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

pith.paper-citation-record.v1
2505.01092 v1

Coverage vector

measured 30 of 30 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-16T04:36:21.879216Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy21
unresolved8
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External citation measurements

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Outbound references

Observation d7112bd6-49fe-44cc-8d2d-bb1e81d64cb2 · outbound

This paper cites First-order methods in optimization.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions First-order methods in optimization

Reference 1

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Observation cd8ae986-d6c2-45c4-a21f-8d49f2507b87 · outbound

This paper cites Nonlinear Programming.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Nonlinear Programming

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.435395Z

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Observation bd2d22fb-e5f6-4441-8a0d-0c0d326336b5 · outbound

This paper cites A H\"olderian backtracking method for min-max and min-min problems.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A H\"olderian backtracking method for min-max and min-min problems

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:36:22.015986Z

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Observation 70f6b5ce-306a-4fc3-98f4-2a04e1817860 · outbound

This paper cites The iterates of the frank–wolfe algorithm may not converge.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions The iterates of the frank–wolfe algorithm may not converge

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.421729Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b250fbcd-ab53-4bb4-af24-32a0e42bf4c3 · outbound

This paper cites Conditional Gradient Methods.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Conditional Gradient Methods

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T04:36:21.757917Z

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Observation f29ab3a0-99db-4898-b3b5-0a2b2c89db64 · outbound

This paper cites A generalized c onditional gradient method and its connection to an iterative shrinkage method.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A generalized c onditional gradient method and its connection to an iterative shrinkage method

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.403964Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 48b0591f-6b56-434d-a14d-ae8072cd1543 · outbound

This paper cites Coresets, sparse greedy approximation, and the frank-wolfe algorithm.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Coresets, sparse greedy approximation, and the frank-wolfe algorithm

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.386909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a587b11-4a5a-4384-af97-ef8cee600378 · outbound

This paper cites Complexity of linear min imization and projection on some sets.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Complexity of linear min imization and projection on some sets

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.370107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc4b24b9-b660-45ad-a5e8-689ca985e53a · outbound

This paper cites Proximal gradient methods beyond monotony.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Proximal gradient methods beyond monotony

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.343180Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 639828c6-0bc4-402f-8aa8-8608df836a1a · outbound

This paper cites Proximal gradient algo rithms under local lipschitz gradi- ent continuity: A convergence and robustness analysis of panoc.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Proximal gradient algo rithms under local lipschitz gradi- ent continuity: A convergence and robustness analysis of panoc

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.322026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a0c3eff7-252a-4054-a604-9cdffa3ec345 · outbound

This paper cites Rates of convergence for conditional gradien t algorithms near singular and nonsingular extremals.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Rates of convergence for conditional gradien t algorithms near singular and nonsingular extremals

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.302086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 251bc72c-c608-4e1e-9fee-405537b17728 · outbound

This paper cites Conditional gradient algorit hms with open loop step size rules.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Conditional gradient algorit hms with open loop step size rules

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.282437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5111a5b9-1397-4ba4-b5d4-5a979055e72f · outbound

This paper cites An algorithm for quadratic pro gramming.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions An algorithm for quadratic pro gramming

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.261733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 09626068-0010-46ef-ac6c-a4b28ad98135 · outbound

This paper cites A generalized proximal point a lgorithm for certain non-convex minimization problems.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A generalized proximal point a lgorithm for certain non-convex minimization problems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.247357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 839a3cdf-a230-4bc0-8aff-ef0bdc85cfb5 · outbound

This paper cites Conditional gradient type methods for composit e nonlinear and stochastic optimization.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Conditional gradient type methods for composit e nonlinear and stochastic optimization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.231128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b523dcda-016e-4344-b0a7-dfd80d116c69 · outbound

This paper cites A parameter-free c onditional gradient method for composite minimization under h¨ older condition.Journal of Machine Learning Research , 24(166):1–34, 2023.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A parameter-free c onditional gradient method for composite minimization under h¨ older condition.Journal of Machine Learning Research , 24(166):1–34, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.213534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 256d65a3-523a-404f-9291-bf8302bd4ffe · outbound

This paper cites Revisiting frank-wolfe: Projection-free sparse convex optimization.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Revisiting frank-wolfe: Projection-free sparse convex optimization

Reference 17

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Source-reported events for the cited work

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Observation 31a3243f-9524-4f30-a9de-c58789b6aade · outbound

This paper cites Convergence analysis of the proximal gradient method in the presence of the kurdyka–/suppress lojasiewicz property without global lipschitz assumptions.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Convergence analysis of the proximal gradient method in the presence of the kurdyka–/suppress lojasiewicz property without global lipschitz assumptions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.169092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 194d4dc9-cfc2-4cdb-ad04-1f42c14d9052 · outbound

This paper cites Structur ed nonconvex and nonsmooth opti- mization: algorithms and iteration complexity analysis.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Structur ed nonconvex and nonsmooth opti- mization: algorithms and iteration complexity analysis

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.150168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ba310c9-0b81-4558-8938-d89b2f177e36 · outbound

This paper cites Convergence of Nonmonotone Proximal Gradient Methods under the Kurdyka-Lojasiewicz Property without a Global Lipschitz Assumption.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Convergence of Nonmonotone Proximal Gradient Methods under the Kurdyka-Lojasiewicz Property without a Global Lipschitz Assumption

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:36:21.822079Z

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Unavailable: canonical work link unavailable.

Observation fa8a98b7-d3be-443c-ba6f-46881ab8953e · outbound

This paper cites Convergence properties of monotone and nonmonotone proximal gradient methods revisited.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Convergence properties of monotone and nonmonotone proximal gradient methods revisited

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.133868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d12f4588-936b-47e7-a1fd-0f534cd4f493 · outbound

This paper cites On fast convergence rates for generalized conditional gradient methods with backtracking stepsize.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions On fast convergence rates for generalized conditional gradient methods with backtracking stepsize

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.118227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bade5d21-c52b-4273-9383-763f7ab44239 · outbound

This paper cites Convergence Rate of Frank-Wolfe for Non-Convex Objectives.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Convergence Rate of Frank-Wolfe for Non-Convex Objectives

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T04:36:21.838730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f16333d3-2a24-4ef6-9701-c9984e886463 · outbound

This paper cites Constrained minimization meth ods.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Constrained minimization meth ods

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.099851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa7c1731-0787-4f46-9a2a-7646a44b79cf · outbound

This paper cites A minimization method for the s um of a convex function and a continuously diﬀerentiable function.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A minimization method for the s um of a convex function and a continuously diﬀerentiable function

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.076147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c0ba493-aa93-4002-b694-668dce21e184 · outbound

This paper cites Complexity bounds for primal-dual methods minimiz ing the model of objective function.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Complexity bounds for primal-dual methods minimiz ing the model of objective function

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T04:36:21.855393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 042e6104-1cb9-400d-be90-662e561bb9bc · outbound

This paper cites Fast Frank--Wolfe Algorithms with Adaptive Bregman Step-Size for Weakly Convex Functions.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Fast Frank--Wolfe Algorithms with Adaptive Bregman Step-Size for Weakly Convex Functions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T04:36:21.860248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 967815da-d7f0-4e67-9ef1-9dbbcdab248f · outbound

This paper cites A nonmonotone con- ditional gradient method for multiobjective optimization problems.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A nonmonotone con- ditional gradient method for multiobjective optimization problems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.048389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b943bb35-c171-48cb-838f-e84e9668a9bc · outbound

This paper cites Proximal gradient-type method with generalized distance and convergence analysis without global descent lemma.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions Proximal gradient-type method with generalized distance and convergence analysis without global descent lemma

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T04:36:21.870692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc20468a-26ec-40b6-a2c1-1c9b6de3555e · outbound

This paper cites A nonmonotone line search te chnique and its application to unconstrained optimization.

Convergence of linesearch-based generalized conditional gradient methods without smoothness assumptions A nonmonotone line search te chnique and its application to unconstrained optimization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:36:22.031362Z

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