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Paper Citation Record · LEDGER

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable

As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.02131.

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

pith.paper-citation-record.v1
2507.02131 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:45:12.939342Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

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

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

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  • verified fuzzy49
  • unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4019962a-83ed-4129-b8a9-451a02ac9056 · outbound

This paper cites Natural gradient works efficiently in learning.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Natural gradient works efficiently in learning

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 999271e3-3eaa-4553-90cc-4ce0b3292191 · outbound

This paper cites Why natural gradient? In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , volume 2, pages 1213–1216, 1998.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Why natural gradient? In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , volume 2, pages 1213–1216, 1998

Reference 2

Resolution
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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b96b07d7-fa4b-442e-bf05-bbd761e99cb2 · outbound

This paper cites Intrinsic robustness of global asymptotic stability.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Intrinsic robustness of global asymptotic stability

Reference 3

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 7eaf27d4-9a56-427a-be3f-9c70c3dcb97c · outbound

This paper cites Robust accelerated gradient methods for smooth strongly convex functions.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Robust accelerated gradient methods for smooth strongly convex functions

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation e7440cc7-edd5-4ab8-8ea9-ec991c929b0e · outbound

This paper cites Nonlinear Programming.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Nonlinear Programming

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation cd20acbc-cb7f-4d1d-a109-b61f8e1523fa · outbound

This paper cites Bertsekas and John N.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Bertsekas and John N

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 0f0fb140-d289-44b5-9538-15fdd4b0f1be · outbound

This paper cites Bertsekas and John N.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Bertsekas and John N

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation be855d86-ddfa-458a-9178-2dc3fc2ba59b · outbound

This paper cites Poveda, and Emiliano Dall’Anese.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Poveda, and Emiliano Dall’Anese

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9175930b-44d0-4652-b7cf-9f569b2d61db · outbound

This paper cites On Topological and Metrical Properties of Stabilizing Feedback Gains: the MIMO Case.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable On Topological and Metrical Properties of Stabilizing Feedback Gains: the MIMO Case

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a7cdc2d0-2449-4b25-aab7-3b91cc0ff182 · outbound

This paper cites Policy Gradient-based Algorithms for Continuous-time Linear Quadratic Control.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Policy Gradient-based Algorithms for Continuous-time Linear Quadratic Control

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T20:45:12.633866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation db794b79-1aae-420d-91fc-10fcb94a2f3e · outbound

This paper cites The role of convexity in saddle-point dynamics: Lyapunov function and robustness.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable The role of convexity in saddle-point dynamics: Lyapunov function and robustness

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 23d14a74-a7a5-4ec0-8d62-f71cbf6a4adc · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 12

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 4028b3bb-5135-490c-80ec-1b507d9bcdb5 · outbound

This paper cites Input-to-state stability of a bilevel proximal gradient descent algorithm.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Input-to-state stability of a bilevel proximal gradient descent algorithm

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b012db8f-86ac-46b7-93bf-c426a387724b · outbound

This paper cites A robust accelerated optimization algorithm for strongly convex functions.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable A robust accelerated optimization algorithm for strongly convex functions

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 51bee6ce-6aaf-4aa4-90dc-d10ceecc7e87 · outbound

This paper cites First- order methods of smooth convex optimization with inexact oracle.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable First- order methods of smooth convex optimization with inexact oracle

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation e4d4c17c-9fbb-4761-abea-6b04ee2fe145 · outbound

This paper cites Global convergence of policy gradient methods for the linear quadratic regulator.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Global convergence of policy gradient methods for the linear quadratic regulator

Reference 16

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 8e072483-5ea6-44e2-a4f4-57c2f2787876 · outbound

This paper cites Convex open subsets of Rn are homeomorphic to n-dimensional open balls.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Convex open subsets of Rn are homeomorphic to n-dimensional open balls

Reference 17

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d7c8da69-1c98-4884-b743-73bbd93c9b18 · outbound

This paper cites On a Newton-like method for solving algebraic Riccati equations.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable On a Newton-like method for solving algebraic Riccati equations

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d0dfad06-2497-4aa6-81e6-438859f0012e · outbound

This paper cites Stability of Motion.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Stability of Motion

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.805104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a9a71c16-27cb-47fe-97a0-ddb407f3e78f · outbound

This paper cites Dissipativity theory for Nesterov’s accelerated method.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Dissipativity theory for Nesterov’s accelerated method

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.786798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation dd2d125b-618a-41b7-9837-943891c47a9d · outbound

This paper cites Toward a theoretical foundation of policy optimization for learning control policies.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Toward a theoretical foundation of policy optimization for learning control policies

Reference 21

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 35fd2368-b261-47b8-bc56-9ec91759050d · outbound

This paper cites Robust Adaptive Dynamic Programming.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Robust Adaptive Dynamic Programming

Reference 22

Resolution
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raw_fallback, observed 2026-08-06T20:45:13.745815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation dbe865fe-925a-4d6a-8002-caa5c87faf5a · outbound

This paper cites Learning- based control: A tutorial and some recent results.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Learning- based control: A tutorial and some recent results

Reference 23

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation cf2b99d2-ddb4-4653-8fa7-55a70f83bd95 · outbound

This paper cites Teel, and Laurent Praly.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Teel, and Laurent Praly

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.710621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5baed8f2-cc54-44d8-a602-d778740a65c0 · outbound

This paper cites Input-to-state stability for discrete-time nonlinear systems.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Input-to-state stability for discrete-time nonlinear systems

Reference 25

Resolution
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raw_fallback, observed 2026-08-06T20:45:13.693820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b51150e6-2443-4396-9f99-7addf3c5a6af · outbound

This paper cites A converse Lyapunov theorem for discrete-time systems with disturbances.Systems & Control Letters , 45(1):49–58, 2002.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable A converse Lyapunov theorem for discrete-time systems with disturbances.Systems & Control Letters , 45(1):49–58, 2002

Reference 26

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 16ea31a4-9822-4321-a7d6-e71f5ce3fb49 · outbound

This paper cites Dynamical, symplectic and stochastic perspectives on gradient-based optimization.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Dynamical, symplectic and stochastic perspectives on gradient-based optimization

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.655528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation c56d7e03-92c5-4e48-b078-afa1192b1014 · outbound

This paper cites Contributions to the theory of optimal control.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Contributions to the theory of optimal control

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.636984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 37c57029-541e-4b73-a109-666a42f80ef4 · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the Polyak- Lojasiewicz condition.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Linear convergence of gradient and proximal-gradient methods under the Polyak- Lojasiewicz condition

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.617829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.744059Z digest=sha256:30d613210c649cecd4dbf232d84c6ff715e2a90e67daf4525ad9ef8d0639a069

Observation 10c9d03d-7308-4d35-b9c9-5c48c71cdf24 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Adam: A Method for Stochastic Optimization

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T20:45:12.749598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:45:12.749598Z digest=sha256:4e916fb7324fe3e42a33b356db78bd8a0a9e3afad7f26101ce76d5ae19822d3d

Observation a71d7601-e957-4c7c-8e65-413851ecd323 · outbound

This paper cites Kleinman.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Kleinman

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.601820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 3894cd58-7db7-49f9-905c-9d2b61d9e4fc · outbound

This paper cites Actor-critic algorithms.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Actor-critic algorithms

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.580992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.759996Z digest=sha256:38f9b53d4de29e6be023a58c05c69510f2fabc1ba275351b80b4b092187dd321

Observation 59a6bae2-7a33-4ce1-8c51-fdd5e980a2c7 · outbound

This paper cites Analysis and design of optimization algorithms via integral quadratic constraints.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Analysis and design of optimization algorithms via integral quadratic constraints

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.562526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.765325Z digest=sha256:9749ddf6af95ef6255522b292daa2809039b8cf2a99c9fea7146525823cd9cc4

Observation fb9eaf88-1f2f-4f50-9e6e-9c9db6f543af · outbound

This paper cites Levine and Michael Athans.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Levine and Michael Athans

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.544113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.771220Z digest=sha256:b273650b7064c0a6524bb6f60e9b894845c71e03f99e7a62f3844d6c259b894d

Observation c90f5fc9-214f-4003-858e-5f51a2984132 · outbound

This paper cites Distributed reinforcement learning for decentralized linear quadratic control: A derivative-free policy optimization approach.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Distributed reinforcement learning for decentralized linear quadratic control: A derivative-free policy optimization approach

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.526285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.777087Z digest=sha256:52c17b568e0a86a65a805e0989a95e7d3a394b0713f329267d6952935120b9bc

Observation 50667f4e-144f-46f2-8dbe-2fe561bb7f25 · outbound

This paper cites Continuous control with deep reinforcement learning.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Continuous control with deep reinforcement learning

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T20:45:12.783504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:45:12.783504Z digest=sha256:43dcdd9beb89819f49abc0dc4d645f6f51a2829875ab630acf4e4dad62794561

Observation 5214e0e1-8790-4643-9938-238424868ae4 · outbound

This paper cites A topological property of real analytic subsets (in French).

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable A topological property of real analytic subsets (in French)

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.509192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.792542Z digest=sha256:23cfd52372a96c6d790608bb4a55c4e215d54e91a328d28c878cb30fedd431f6

Observation 11e386de-8597-4cc1-98a5-b78fc902733e · outbound

This paper cites Computational methods for parametric LQ problems–A survey.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Computational methods for parametric LQ problems–A survey

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.493900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.798310Z digest=sha256:d83696a47b3b2e44054a1a598c9687572a95472a216b808c809551ab0dbeda50

Observation d8172d8f-52ea-4034-a86b-e6fe6b31dbd9 · outbound

This paper cites Jovanovi´ c.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Jovanovi´ c

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.476911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.804193Z digest=sha256:4b9b41d47f5d9935f041cb33a159088c400ddadc3e8c50bd31255a6f4b632f77

Observation 76c478a3-8a5f-48f1-a808-c54f31599749 · outbound

This paper cites Jovanovi´ c.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Jovanovi´ c

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.459655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.811038Z digest=sha256:6104f9841d931960bce7473b7f1ffb319546893cde3f6bd7876c7be0c5c9e75d

Observation 0f85367e-a48b-42c0-8d1c-35ce48362f59 · outbound

This paper cites A systematic approach to Lyapunov analyses of continuous- time models in convex optimization.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable A systematic approach to Lyapunov analyses of continuous- time models in convex optimization

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.443427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.816691Z digest=sha256:d9bb514a3aeb115b91fa17169e7cf18b954dbd917456cf91251cbad7a39134ab

Observation d25a08f3-bb37-4652-8ba6-c7633a772090 · outbound

This paper cites Introductory Lectures on Convex Optimization: A Basic Course , volume 87.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Introductory Lectures on Convex Optimization: A Basic Course , volume 87

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.426749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.822874Z digest=sha256:4a3027a817d6a2c4827a5b354d0243d39a5c83d0cab2a299e310cde83a922a42

Observation e736fc66-6d01-40ea-8d40-c6a9ad01c242 · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.409918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.829307Z digest=sha256:f5af4f2e2d002235e14bbbf8cf660205ef70dd86fefdcafa5934327be66d0c7d

Observation f1ad3848-a1cd-4d11-b936-b63aab38a848 · outbound

This paper cites Robust policy iteration for continuous-time linear quadratic regulation.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Robust policy iteration for continuous-time linear quadratic regulation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.393241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.836030Z digest=sha256:a3ac886835c4f59919a0719a236230279b7f74641b96d02d6fdc16eae15a6d6e

Observation 4318f1e7-4995-441e-92cf-25060c7bcef4 · outbound

This paper cites Robust reinforcement learning: A case study in linear quadratic regulation.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Robust reinforcement learning: A case study in linear quadratic regulation

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.371538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.841573Z digest=sha256:341c1d2372b12e028e8ccc82d5b9053104d962b0b4efd9ebaefe9660f550967b

Observation f52f9c84-9dbd-48b4-8f5a-9d3be1828dc6 · outbound

This paper cites The Matrix Cookbook, October 2008.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable The Matrix Cookbook, October 2008

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.352957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.846951Z digest=sha256:141862506c4d92efc71b30fcc4a981abdde5ccad831f54bf7d9f386a0a92ebea

Observation 6e57c7a3-0010-4314-b93c-a9f00b3fc389 · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.335404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.852905Z digest=sha256:ec742db03e43c67ab034b8d9d31e9df943574d67e177b8b3fdfebf052a7f5309

Observation 522cceb8-a2be-4012-ad8c-7c87fd1e4e43 · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.319946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.857842Z digest=sha256:e4a112bb0d6664452288a12a4cfd06e59a368d39b429bcc770b599ec91cd58a8

Observation bb844e5a-9b80-440c-abee-0fa777322a4a · outbound

This paper cites Poveda and Miroslav Krsti´ c.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Poveda and Miroslav Krsti´ c

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.304422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.862754Z digest=sha256:803b75b950bbb356fcca2d43edbc0098651bf603e8fd094a9485cd8b77df8de7

Observation 0ddc2ee8-46fa-4b6f-ab8a-83b476d948ae · outbound

This paper cites Trust region policy optimization.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Trust region policy optimization

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.289006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.868315Z digest=sha256:d99bf0b039bd19a3b6e4c4ce50e131b1c76afe09ea3427aab757e1e9d7df249a

Observation a8b8f215-5c0a-409b-ac3e-accfebf6859e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Proximal Policy Optimization Algorithms

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T20:45:12.873024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:45:12.873024Z digest=sha256:988583c039495a5fc0c99ac12bd7e2f6b86d28727516f1847e8de5f2b1555aa7

Observation c50d164f-ac3e-44d7-bf67-99fb1c607b16 · outbound

This paper cites Error stability properties of generalized gradient-type algorithms.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Error stability properties of generalized gradient-type algorithms

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.270111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.879368Z digest=sha256:ab2d4cfb89f7c22780529690530e1701127965e5aa6ee5f3f8de2a8ad62ab71a

Observation 6419b562-5a10-45d9-b89d-977eca8bfb1e · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.252592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.885381Z digest=sha256:1fb9aba6125c46c1294d6021feac6efe932692bc160555e9e80718fb9d2e7959

Observation 851de88d-8042-4e74-b2e2-bf24074db82d · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.235398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.890327Z digest=sha256:6c36d45ce04b4481fe4c210a34240b0db911db512005ffe34212be12cccd2152

Observation 14191f79-04d1-4c48-ad55-0865ede48d24 · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.219659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.895755Z digest=sha256:1e20794f1542caa6bbaf2b5166c2c312394c776964fd1e4e25e7e05e9103a636

Observation fa8790fb-ef6d-4a83-bf25-418212936911 · outbound

This paper cites an unresolved cited work.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:45:13.203682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.901531Z digest=sha256:c77767c67c6761b635b6bfef4cd250e32d6f277c0f1d92364f5aeb053496d521

Observation 95bbea3b-6883-4778-8aec-5493d388dd5a · outbound

This paper cites Robustness and averaging properties of a large-amplitude, high-frequency extremum seeking control scheme.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Robustness and averaging properties of a large-amplitude, high-frequency extremum seeking control scheme

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.186332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.907140Z digest=sha256:7dde07cd6c27609a0979b6da3a2c4f34433e9d42626dfb78f7ac59e5b9821cf6

Observation 8e6322a2-f4d4-4d88-bc37-dadab952639e · outbound

This paper cites Sutton and Andrew G.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Sutton and Andrew G

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.166994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.914870Z digest=sha256:dc9023398d690f106a8b63ac5895cad2c62696f95c05caa828634d1078df5d81

Observation 20a424fe-ebb3-4bd9-a6be-a2a262212b35 · outbound

This paper cites Trace bounds on the solution of the algebraic matrix Riccati and Lyapunov equation.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Trace bounds on the solution of the algebraic matrix Riccati and Lyapunov equation

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.151397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.920442Z digest=sha256:5b88410e6e856e163abac6dc6aeb157d9e020109374a1c869c26fd5048131a73

Observation 1309a86b-5fc1-44b0-b692-2017633ee5e4 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.134619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.926916Z digest=sha256:f7e0a77601f2a67b2b068b48b2542cc3f4473a5b96c154eefab292c0f3d25dda

Observation c824cba6-82f2-4f86-b1a6-546ac7a88f83 · outbound

This paper cites Wilson, Ben Recht, and Michael I.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Wilson, Ben Recht, and Michael I

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.114022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.932360Z digest=sha256:2fc78e8b0f25138882e6cace96ee3b48cc07061e7a6d9b454ac227cf3f6dff70

Observation 9968761c-2e80-410f-8a8b-cfd4a49b15a5 · outbound

This paper cites Wesley Wilson.

Perturbed Gradient Descent Algorithms are Small-Disturbance Input-to-State Stable Wesley Wilson

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:45:13.092738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:45:12.939342Z digest=sha256:28a0f6fdc75168e5648ff218d6d52ca962fff18531566b030d8e3335aadbbc9d

Pith citing papers

No inbound Pith citation observations are available.