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Paper Citation Record · LEDGER

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

As of 11 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 2026-08-11T06:34:44.6726+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
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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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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
verified fuzzy
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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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
raw_fallback, observed 2026-08-06T20:45:14.008641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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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unresolved
no resolver link, observed 2026-08-06T20:45:12.626378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:45:12.626378Z digest=sha256:062626a2f4c6027f220fcdfe72bd4c452db4483397cd32d311cd1db3d83c393d

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

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unresolved
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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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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
raw_fallback, observed 2026-08-06T20:45:13.912461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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
raw_fallback, observed 2026-08-06T20:45:13.845775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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
verified fuzzy
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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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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raw_fallback, observed 2026-08-06T20:45:13.766428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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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raw_fallback, observed 2026-08-06T20:45:13.726637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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
verified fuzzy
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 2026-08-11T06:34:44.6726+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
raw_fallback, observed 2026-08-06T20:45:13.675025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.744059Z digest=sha256:0853931c2befc07f1c416394c48f9039232dddb315df10428fae3bcd77cd1ce1

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.754981Z digest=sha256:d253beeaf7ba00b62123b6fe5045f08f16de405c2d9009b5c31a74350395d240

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.759996Z digest=sha256:51efd2c0b62f90f03fb7382971ef57f0454ca9424a177f9e6f6815eb0dabc8c5

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.765325Z digest=sha256:70b55d9c7e05d032867815510204ee512245d35710f2aa9fd7611050f89484f7

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

Resolution
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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.771220Z digest=sha256:840b8c8bd4fcb2990e9b9b1bd560efd2e673ccf3719687f13c41afecfeac6199

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.777087Z digest=sha256:a6c17682ae5a124334eab87d3d97e90bdeaf952e121a91f2741ab57f1e230dbb

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.792542Z digest=sha256:ddc31b4a7d06820c309483363e3a23180987d48fe2eb60a73710cb7534a1ed25

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.798310Z digest=sha256:e8fb60b4f781e5354a7bcf667477df888c1e09872a7f160bfb1c73fab53039f0

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.804193Z digest=sha256:d7ecb7d4dce2401d33de14d95f7dcf23cf46e916ec819d791238366187287cfe

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.811038Z digest=sha256:136588db1fad436e6dfc16131af5ee15a45dd7437abc1f8c1f54ca59fc584e63

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.816691Z digest=sha256:aade8049833bf11ffec6096ed6c9cb42346bb507314fb0188ee199d330189a8a

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.822874Z digest=sha256:83f614139e46809bf25455099d2cbf3c748e3b1fade4693f8c361ea4d06f2338

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.829307Z digest=sha256:5958fc5790b8697d12cf4eb3b8706553085cc32fa103150141195843eb424601

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.836030Z digest=sha256:0ed64d053c3af3f16c1cc60f225c4cd486356962f6904413ccb16e6d1a07a44c

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.841573Z digest=sha256:f8839d6805983fc5bc2cf7f2557c6cca4c0951d80835c155128ebd3d98aa464e

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.846951Z digest=sha256:40ad7dd0919b05f486e8f950cda170a31fb22537808ccd8e5b34e3b90335f269

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.852905Z digest=sha256:f39929ad8b402922f98e6f87cd4e38f38a3b6decb5b03bb056106fcfe192774c

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.857842Z digest=sha256:66cd63d5ea43d236164942a480a6233f0905f2d0a68ab6cba173d3a208f81a1d

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.862754Z digest=sha256:72bda2fb6afd9fdb9d306a99f5193ab06d8ec0600ae7004bd1bbe34c7570b82e

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.868315Z digest=sha256:6e3ea1bad3824b19aee15a10d28bb52cd99c2afa24604af484d87efaa3fb57c5

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.879368Z digest=sha256:96e8774e4090b6e064f87a3b8a1d47d5fcbab40d38b491ade3e6b51b5bcc0ea9

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.885381Z digest=sha256:3ec2441322a9d0fc7ecb9114bda2418e057e6f2e79b1450f1bde805a2d8f3bb3

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.890327Z digest=sha256:38d8b0d2b5009f7b27b26afcec8492e59d845b993dcddaa0a18b99b8e6815503

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.895755Z digest=sha256:40cdd8eb9a51e6fad7daaf4bf07d00717618484fc51c76ed4b7590bf6b4d988f

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.901531Z digest=sha256:1a375f56a017941791f467dc90b607d68c73ab0ce433170d5b6e546600c918e1

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.907140Z digest=sha256:da8fd89086c1ad5d6618067c252e3cc1bff727f2c2809b864d39f5101277582a

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.914870Z digest=sha256:4592208340b2cbdc676e03ec62d8681ec532eb38b74c565041f5d53b92f0f151

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.920442Z digest=sha256:03d99004a5c8ca27480b871e02ed1a7c699dd30082481fa4a24b785511b3cbe4

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.926916Z digest=sha256:038f8300acfce9f386e3be31da60ae79818b5fc49e14aeb94971a43162d556ab

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.932360Z digest=sha256:38994a5d62e0afbc2656ccdd5eac340485308ece0a94f003a9667b450aa5fe88

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T20:45:12.939342Z digest=sha256:ea9d715bbe18da578e9aa5836a5360976ada137e187861bf77b63bd9c3de4126

Pith citing papers

No inbound Pith citation observations are available.