Stability of first-order methods in tame optimization | Paper Citation Record

Observation 19036b5b-88f1-437f-b84f-444520c83231 · outbound

This paper cites Deep residual learning for image recognition,.

Stability of first-order methods in tame optimization Deep residual learning for image recognition,

Reference 1

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Stability of first-order methods in tame optimization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

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This paper cites Attention is all you need,.

Stability of first-order methods in tame optimization Attention is all you need,

Reference 3

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Stability of first-order methods in tame optimization BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

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This paper cites Language models are few-shot learners,.

Stability of first-order methods in tame optimization Language models are few-shot learners,

Reference 5

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This paper cites High-resolution image synthesis with latent diffusion models,.

Stability of first-order methods in tame optimization High-resolution image synthesis with latent diffusion models,

Reference 6

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Observation 71d64fba-6860-40c1-a6c8-93ed29e62348 · outbound

This paper cites A stochastic approximation method,.

Stability of first-order methods in tame optimization A stochastic approximation method,

Reference 7

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This paper cites Adam: A Method for Stochastic Optimization.

Stability of first-order methods in tame optimization Adam: A Method for Stochastic Optimization

Reference 8

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This paper cites An overview of gradient descent optimization algorithms.

Stability of first-order methods in tame optimization An overview of gradient descent optimization algorithms

Reference 9

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This paper cites Large scale distributed deep networks,.

Stability of first-order methods in tame optimization Large scale distributed deep networks,

Reference 10

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Observation 135f1662-ed9d-420d-aab6-17725ec68544 · outbound

This paper cites Problème général de la stabilité du mouvement,.

Stability of first-order methods in tame optimization Problème général de la stabilité du mouvement,

Reference 11

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This paper cites Sastry, Nonlinear systems: analysis, stability, and control.

Stability of first-order methods in tame optimization Sastry, Nonlinear systems: analysis, stability, and control

Reference 12

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Observation 88b54bc1-066c-4595-8178-5f6e730c50ff · outbound

This paper cites van den Dries, Tame topology and o-minimal structures.

Stability of first-order methods in tame optimization van den Dries, Tame topology and o-minimal structures

Reference 13

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Observation 59f497b6-a15c-4cb6-97c6-cab0ee5fe558 · outbound

This paper cites An invitation to tame optimization,.

Stability of first-order methods in tame optimization An invitation to tame optimization,

Reference 14

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Observation ece95287-38b6-40b3-b775-a9089d7fca31 · outbound

This paper cites Subdifferentiability of real functions,.

Stability of first-order methods in tame optimization Subdifferentiability of real functions,

Reference 15

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Observation 7d7c9330-3a56-4a34-94b0-080d9194e737 · outbound

This paper cites Friedman, T.

Stability of first-order methods in tame optimization Friedman, T

Reference 16

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Observation f6297abd-f57f-4abd-9b11-a2fd99b0e07c · outbound

This paper cites Generalized gradients and applications,.

Stability of first-order methods in tame optimization Generalized gradients and applications,

Reference 17

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Stability of first-order methods in tame optimization Unresolved cited work

Reference 18

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Observation 4b078cea-09af-49bc-bc4c-bad8364039b0 · outbound

This paper cites Aubin and A.

Stability of first-order methods in tame optimization Aubin and A

Reference 19

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Stability of first-order methods in tame optimization Unresolved cited work

Reference 20

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Observation 9c5d8914-aacf-4af3-87b9-e424448ea161 · outbound

This paper cites Remarks on Tarski’s problem concerning ( R,+,∗, exp),.

Stability of first-order methods in tame optimization Remarks on Tarski’s problem concerning ( R,+,∗, exp),

Reference 21

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Observation 5e6b472e-31f7-43da-a3b8-4024ed23e3ea · outbound

This paper cites Definable sets in ordered structures. i,.

Stability of first-order methods in tame optimization Definable sets in ordered structures. i,

Reference 22

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Observation 03832b93-4e37-4ffd-8d7b-b5e11fa7ae1c · outbound

This paper cites Bochnak, M.

Stability of first-order methods in tame optimization Bochnak, M

Reference 23

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Observation 6fa5d605-a8b6-4e15-a70d-fab7b4883b6f · outbound

This paper cites Geometric categories and o-minimal structures,.

Stability of first-order methods in tame optimization Geometric categories and o-minimal structures,

Reference 24

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This paper cites Division d’une distribution par une fonction analytique de variables réelles,.

Stability of first-order methods in tame optimization Division d’une distribution par une fonction analytique de variables réelles,

Reference 25

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This paper cites Sur le problème de la division,.

Stability of first-order methods in tame optimization Sur le problème de la division,

Reference 26

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Observation cd46f27d-25d6-4402-9c7a-2e6c042cb24a · outbound

This paper cites On the division of distributions by polynomials,.

Stability of first-order methods in tame optimization On the division of distributions by polynomials,

Reference 27

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Observation dc4b9d73-287c-4af2-a600-9a0466b31f97 · outbound

This paper cites On gradients of functions definable in o-minimal structures,.

Stability of first-order methods in tame optimization On gradients of functions definable in o-minimal structures,

Reference 28

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This paper cites Clarke subgradients of stratifiable func- tions,.

Stability of first-order methods in tame optimization Clarke subgradients of stratifiable func- tions,

Reference 29

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This paper cites Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka- Łojasiewicz inequality,.

Stability of first-order methods in tame optimization Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka- Łojasiewicz inequality,

Reference 30

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Observation 697c654a-dd46-4ab4-9f85-51148f4c24d4 · outbound

This paper cites Global convergence of the gradient method for functions definable in o-minimal structures,.

Stability of first-order methods in tame optimization Global convergence of the gradient method for functions definable in o-minimal structures,

Reference 31

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Observation d667e5ce-180f-4aff-a2bd-7804d1f3dfbc · outbound

This paper cites Curves of descent,.

Stability of first-order methods in tame optimization Curves of descent,

Reference 32

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Observation 778f5260-7ed1-4982-a40f-5935467e61cb · outbound

This paper cites Stochastic subgradient method converges on tame functions,.

Stability of first-order methods in tame optimization Stochastic subgradient method converges on tame functions,

Reference 33

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Observation 3182ff22-64b8-4215-9856-c8155e8bf5fd · outbound

This paper cites Optimization methods for large-scale machine learning,.

Stability of first-order methods in tame optimization Optimization methods for large-scale machine learning,

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Observation 4230129d-424e-4ac6-b199-69ac9246f672 · outbound

This paper cites Nonconvex Robust Low-Rank Matrix Recov- ery,.

Stability of first-order methods in tame optimization Nonconvex Robust Low-Rank Matrix Recov- ery,

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Observation 1e80e234-3ee9-419d-8d1e-c8465ce40ce6 · outbound

This paper cites Global convergence of sub-gradient method for robust matrix re- covery: Small initialization, noisy measurements, and over-parameterization,.

Stability of first-order methods in tame optimization Global convergence of sub-gradient method for robust matrix re- covery: Small initialization, noisy measurements, and over-parameterization,

Reference 36

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Observation a30bb061-1dd6-4754-8d0f-5a6feba7491c · outbound

This paper cites Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion,.

Stability of first-order methods in tame optimization Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion,

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This paper cites Deep learning,.

Stability of first-order methods in tame optimization Deep learning,

Reference 38

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Observation 1483b8ef-053c-4973-94b6-ae4b9bb012c7 · outbound

This paper cites Sur l’Equation à l’Aide de Laquelle on Détermine les Inégalités Sécu- laires,.

Stability of first-order methods in tame optimization Sur l’Equation à l’Aide de Laquelle on Détermine les Inégalités Sécu- laires,

Reference 39

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This paper cites Continuous time analysis of momentum methods,.

Stability of first-order methods in tame optimization Continuous time analysis of momentum methods,

Reference 40

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Observation fefa8cc5-9c9b-422e-8c57-50ed28b0721f · outbound

This paper cites Some methods of speeding up the convergence of iteration methods,.

Stability of first-order methods in tame optimization Some methods of speeding up the convergence of iteration methods,

Reference 41

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Observation 05fe9f29-8e1b-47d9-bb09-9b0a3189dd0f · outbound

This paper cites Nesterov, Lectures on Convex Optimization.

Stability of first-order methods in tame optimization Nesterov, Lectures on Convex Optimization

Reference 42

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This paper cites Shor, “Application of the gradient method for the solution of network transportation problems.

Stability of first-order methods in tame optimization Shor, “Application of the gradient method for the solution of network transportation problems

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Observation 6e97949e-7543-4ac0-9366-956e877e680f · outbound

This paper cites On the structure of algorithms for numerical solution of problems of optimal planning and design,.

Stability of first-order methods in tame optimization On the structure of algorithms for numerical solution of problems of optimal planning and design,

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Observation 7b4c0c11-94c1-4ecc-81ce-a063a51747ef · outbound

This paper cites Bertsekas, Convex optimization algorithms.

Stability of first-order methods in tame optimization Bertsekas, Convex optimization algorithms

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Observation 5b2e4baf-8c10-4f37-8bc6-94a6f8dcf9be · outbound

This paper cites A method for solving the convex programming problem with convergence rate𝑂(1/𝑘2),.

Stability of first-order methods in tame optimization A method for solving the convex programming problem with convergence rate𝑂(1/𝑘2),

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This paper cites Heavy-ball method in nonconvex optimization problems,.

Stability of first-order methods in tame optimization Heavy-ball method in nonconvex optimization problems,

Reference 47

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Observation 6ffc5ce9-5187-4ede-8c6a-c021182acdd5 · outbound

This paper cites iPiano: Inertial proximal algorithm for nonconvex optimization,.

Stability of first-order methods in tame optimization iPiano: Inertial proximal algorithm for nonconvex optimization,

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This paper cites Adaptive switching circuits,.

Stability of first-order methods in tame optimization Adaptive switching circuits,

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This paper cites An adaptive associative memory principle,.

Stability of first-order methods in tame optimization An adaptive associative memory principle,

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Observation 9cc9c2c8-e1b7-4233-a943-26050beb3053 · outbound

This paper cites On the convergence of the lms algorithm with adaptive learning rate for linear feedforward networks,.

Stability of first-order methods in tame optimization On the convergence of the lms algorithm with adaptive learning rate for linear feedforward networks,

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Observation ab4c409b-bde4-42fe-a8e3-57ad00ccc6b5 · outbound

This paper cites Incremental subgradient methods for nondifferentiable op- timization,.

Stability of first-order methods in tame optimization Incremental subgradient methods for nondifferentiable op- timization,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.783361Z

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Observation 34bccf6f-d277-4bb3-a5d8-08365965e127 · outbound

This paper cites Incremental gradient, subgradient, and proximal methods for convex optimization: A survey,.

Stability of first-order methods in tame optimization Incremental gradient, subgradient, and proximal methods for convex optimization: A survey,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.770022Z

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Observation 03213abb-66cf-462a-9daa-e647449597de · outbound

This paper cites Why random reshuffling beats stochas- tic gradient descent,.

Stability of first-order methods in tame optimization Why random reshuffling beats stochas- tic gradient descent,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.756710Z

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Observation e0752615-e712-4016-b399-e8a7670b8e44 · outbound

This paper cites Random reshuffling: Simple analysis with vast improvements,.

Stability of first-order methods in tame optimization Random reshuffling: Simple analysis with vast improvements,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.744034Z

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Observation d7394bbf-1201-4da1-b651-f9a58ef4b1a2 · outbound

This paper cites A unified convergence analysis for shuffling-type gradient methods,.

Stability of first-order methods in tame optimization A unified convergence analysis for shuffling-type gradient methods,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.730757Z

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Observation 3bbb68e2-8ada-49ba-a1bf-7c3cb38a5024 · outbound

This paper cites Incremental without replacement sampling in nonconvex optimization,.

Stability of first-order methods in tame optimization Incremental without replacement sampling in nonconvex optimization,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.717673Z

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Observation 26378519-a92e-4eb8-9183-fc29caefc2c5 · outbound

This paper cites Convergence of random reshuffling under the kurdyka– łojasiewicz inequality,.

Stability of first-order methods in tame optimization Convergence of random reshuffling under the kurdyka– łojasiewicz inequality,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.703477Z

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Observation d9df9354-bfd2-4adb-bfa1-4bab71707da7 · outbound

This paper cites On the importance of initialization and momentum in deep learning,.

Stability of first-order methods in tame optimization On the importance of initialization and momentum in deep learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.688758Z

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Observation 75cb68f0-a135-462a-9a98-caf37fe6db32 · outbound

This paper cites Smg: A shuffling gradient-based method with momentum,.

Stability of first-order methods in tame optimization Smg: A shuffling gradient-based method with momentum,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.675207Z

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Observation cc11de64-e0c3-4517-ae2c-03607392b732 · outbound

This paper cites Nesterov accelerated shuffling gradi- ent method for convex optimization,.

Stability of first-order methods in tame optimization Nesterov accelerated shuffling gradi- ent method for convex optimization,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.660715Z

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Observation 26b2e51a-86ea-4ff9-9480-969112904ec7 · outbound

This paper cites Coordinate descent algorithms,.

Stability of first-order methods in tame optimization Coordinate descent algorithms,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.648016Z

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Observation b22dffe7-49a9-46bc-a16d-e5e1b052b7a2 · outbound

This paper cites an unresolved cited work.

Stability of first-order methods in tame optimization Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:16:52.635925Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a033e2d7-f5e8-492b-9279-a947915cd8ec · outbound

This paper cites On the convergence of the coordinate descent method for convex differentiable minimization,.

Stability of first-order methods in tame optimization On the convergence of the coordinate descent method for convex differentiable minimization,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.623479Z

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Observation f4e401ee-4b6a-4cd5-bd6c-9bca75b94be9 · outbound

This paper cites Efficiency of coordinate descent methods on huge-scale optimization prob- lems,.

Stability of first-order methods in tame optimization Efficiency of coordinate descent methods on huge-scale optimization prob- lems,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.611539Z

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Observation 99a53ffb-f590-42ef-810b-a5139fc34e8f · outbound

This paper cites Randomness and per- mutations in coordinate descent methods,.

Stability of first-order methods in tame optimization Randomness and per- mutations in coordinate descent methods,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.598895Z

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Observation 64bb4810-13b5-4867-8301-63d4df30f919 · outbound

This paper cites On the convergence of block coordinate descent type meth- ods,.

Stability of first-order methods in tame optimization On the convergence of block coordinate descent type meth- ods,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.585422Z

Source-reported events for the cited work

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Observation d0c95008-0ae3-4afb-8d4e-bdaab0c619f4 · outbound

This paper cites Random permutations fix a worst case for cyclic coordinate descent,.

Stability of first-order methods in tame optimization Random permutations fix a worst case for cyclic coordinate descent,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.571651Z

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Observation ecfd80f7-e3d6-4aa7-9567-4b88545d7ac3 · outbound

This paper cites Euler, Institutiones calculi integralis.

Stability of first-order methods in tame optimization Euler, Institutiones calculi integralis

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.557217Z

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Observation 15bf9183-b95b-4d1d-bb99-52d2312744b7 · outbound

This paper cites Blanton, Foundations of Differential Calculus.

Stability of first-order methods in tame optimization Blanton, Foundations of Differential Calculus

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.543294Z

Source-reported events for the cited work

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Observation 03ccd5b5-0f87-4fb9-915d-b611fc0007b1 · outbound

This paper cites an unresolved cited work.

Stability of first-order methods in tame optimization Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:16:52.530348Z

Source-reported events for the cited work

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Observation 108a98fe-153f-4884-80d6-8e042783788c · outbound

This paper cites Analysis of recursive stochastic algorithms,.

Stability of first-order methods in tame optimization Analysis of recursive stochastic algorithms,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.515805Z

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Observation 8bf377a1-275a-4260-b088-25119eb26aa1 · outbound

This paper cites General convergence results for stochastic approximations via weak con- vergence theory,.

Stability of first-order methods in tame optimization General convergence results for stochastic approximations via weak con- vergence theory,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.500949Z

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Observation 3b50eef9-e457-45b1-b373-e82dc28ab35e · outbound

This paper cites Convergence of recursive adaptive and identification procedures via weak convergence theory,.

Stability of first-order methods in tame optimization Convergence of recursive adaptive and identification procedures via weak convergence theory,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.488368Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7e38ce05-1188-44ef-9974-53dbe10c003c · outbound

This paper cites Stochastic approximations and differential inclu- sions,.

Stability of first-order methods in tame optimization Stochastic approximations and differential inclu- sions,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.474584Z

Source-reported events for the cited work

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Observation 59138bed-64d9-4829-b95e-47c1609c4efd · outbound

This paper cites Stochastic approximations and differential inclu- sions, part ii: Applications,.

Stability of first-order methods in tame optimization Stochastic approximations and differential inclu- sions, part ii: Applications,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.461670Z

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Observation 1a7cbc52-e390-4965-8cbd-8c5b814fa7eb · outbound

This paper cites Dynamics of stochastic approximation algorithms,.

Stability of first-order methods in tame optimization Dynamics of stochastic approximation algorithms,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.447968Z

Source-reported events for the cited work

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Observation 557c7be8-ec6c-4e78-b819-4c0e0ee09115 · outbound

This paper cites an unresolved cited work.

Stability of first-order methods in tame optimization Unresolved cited work

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:51.525669Z

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Unavailable: canonical work link unavailable.

Observation 203578d7-d059-4124-8971-5bc6158153f7 · outbound

This paper cites Stochastic methods for composite and weakly convex optimiza- tion problems,.

Stability of first-order methods in tame optimization Stochastic methods for composite and weakly convex optimiza- tion problems,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.426792Z

Source-reported events for the cited work

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Observation f1509e88-a81a-4538-804c-259868866651 · outbound

This paper cites Conservative set valued fields, automatic differentiation, stochas- tic gradient methods and deep learning,.

Stability of first-order methods in tame optimization Conservative set valued fields, automatic differentiation, stochas- tic gradient methods and deep learning,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.411712Z

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Observation 5ae65100-fd7c-44e5-b7c6-c6a0abcb0099 · outbound

This paper cites Random monotone operators and application to stochastic optimization,.

Stability of first-order methods in tame optimization Random monotone operators and application to stochastic optimization,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.398569Z

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Observation d61b1c1d-086d-4b00-b916-22df139f6527 · outbound

This paper cites Adam-family methods for nonsmooth optimiza- tion with convergence guarantees,.

Stability of first-order methods in tame optimization Adam-family methods for nonsmooth optimiza- tion with convergence guarantees,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.385015Z

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Observation fc8e26db-f23d-4a13-8222-3a08e52bc7c6 · outbound

This paper cites Global stability of first-order methods for coercive tame functions,.

Stability of first-order methods in tame optimization Global stability of first-order methods for coercive tame functions,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.370272Z

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Observation 9afd5c15-5abc-44fb-aa51-55b585b8ef21 · outbound

This paper cites Converging multistep methods for initial value problems involving multival- ued maps,.

Stability of first-order methods in tame optimization Converging multistep methods for initial value problems involving multival- ued maps,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.357041Z

Source-reported events for the cited work

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Observation 152df49c-cc30-4820-87a7-d6e75d19754f · outbound

This paper cites an unresolved cited work.

Stability of first-order methods in tame optimization Unresolved cited work

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:51.552713Z

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Observation 1c1d6405-3fc7-4e64-bc4d-240aa15a5c81 · outbound

This paper cites Difference methods for differential inclusions: A survey,.

Stability of first-order methods in tame optimization Difference methods for differential inclusions: A survey,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.335758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 97fffbd4-23d8-46db-880b-e1ee78d5d189 · outbound

This paper cites Subgradient methods for sharp weakly convex functions,.

Stability of first-order methods in tame optimization Subgradient methods for sharp weakly convex functions,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.321997Z

Source-reported events for the cited work

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Observation 9419fa9c-b992-4c3a-b35d-359ed505e719 · outbound

This paper cites Stochastic algorithms with geometric step decay converge linearly on sharp functions.

Stability of first-order methods in tame optimization Stochastic algorithms with geometric step decay converge linearly on sharp functions

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T05:16:51.565603Z

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Observation 26d2bdc3-07dc-4a23-a5f7-43c65b0f89b1 · outbound

This paper cites Low-rank matrix recovery with composite optimization: Good conditioning and rapid convergence,.

Stability of first-order methods in tame optimization Low-rank matrix recovery with composite optimization: Good conditioning and rapid convergence,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.307778Z

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Observation b986769d-3694-48bb-b65c-f1bca667af78 · outbound

This paper cites On the stable equilibrium points of gradient systems,.

Stability of first-order methods in tame optimization On the stable equilibrium points of gradient systems,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.294794Z

Source-reported events for the cited work

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Observation 34ad5a8e-4c8f-462e-a909-864e4eabdb75 · outbound

This paper cites Favorable classes of lipschitz continuous functions in subgradient opti- mization,.

Stability of first-order methods in tame optimization Favorable classes of lipschitz continuous functions in subgradient opti- mization,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.282704Z

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Observation f6ab1e0e-9687-4416-b42b-4be5e1562c96 · outbound

This paper cites Generic differentiability of lipschitzian functions,.

Stability of first-order methods in tame optimization Generic differentiability of lipschitzian functions,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.270680Z

Source-reported events for the cited work

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Observation ab375fe1-0222-42cd-9081-177a62a43f99 · outbound

This paper cites Convergence of the iterates of descent methods for analytic cost functions,.

Stability of first-order methods in tame optimization Convergence of the iterates of descent methods for analytic cost functions,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.257876Z

Source-reported events for the cited work

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Observation 0f0a5b31-ff8f-4cce-9784-158a246934e7 · outbound

This paper cites Abadi et al.

Stability of first-order methods in tame optimization Abadi et al

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.244737Z

Source-reported events for the cited work

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Observation 46348cf6-6adc-4282-a49d-515336733d57 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Stability of first-order methods in tame optimization Pytorch: An imperative style, high-performance deep learning library,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.230859Z

Source-reported events for the cited work

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Observation ff5cf138-5746-49b1-adbf-0754d51c379e · outbound

This paper cites A simple weight decay can improve generalization,.

Stability of first-order methods in tame optimization A simple weight decay can improve generalization,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.216737Z

Source-reported events for the cited work

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Observation cc54ec23-6046-4d70-9b4c-37e1e2885a6b · outbound

This paper cites Regression shrinkage and selection via the lasso,.

Stability of first-order methods in tame optimization Regression shrinkage and selection via the lasso,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.201723Z

Source-reported events for the cited work

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Observation bc081d89-66ae-4624-8e1a-2f01495fc42e · outbound

This paper cites Matrix Completion has No Spurious Local Minimum,.

Stability of first-order methods in tame optimization Matrix Completion has No Spurious Local Minimum,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.188076Z

Source-reported events for the cited work

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Observation bcc4f8ca-460b-491a-934d-1792aaebaba8 · outbound

This paper cites Beck, First-order methods in optimization.

Stability of first-order methods in tame optimization Beck, First-order methods in optimization

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:16:52.176160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f52bec6-236f-4ec4-9cf3-29fd2bcb42a0 · outbound

This paper cites an unresolved cited work.

Stability of first-order methods in tame optimization Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:16:52.163537Z

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