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Paper Citation Record · LEDGER

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization

As of 18 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2602.05504.

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

pith.paper-citation-record.v1
2602.05504 v4

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measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:18:27.617104Z

measured 69 of 69 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:19:22.582981Z

measured 0 of 1 external citation measurements

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

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68 of 68 outbound references displayed

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Outbound references

Observation 8719d0f5-c01a-46c1-ba19-8dda8fa68f96 · outbound

This paper cites Finding approximate local minima faster than gradient descent.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Finding approximate local minima faster than gradient descent

Reference 1

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Observation 78be8a93-5ac5-4639-a1ed-44b1fac6a73a · outbound

This paper cites Katyusha: The first direct acceleration of stochastic gradient methods.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Katyusha: The first direct acceleration of stochastic gradient methods

Reference 2

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Observation e9d15d56-88e2-4991-8b5e-a1b024d34f68 · outbound

This paper cites Neon2: Finding local minima via first-order oracles.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Neon2: Finding local minima via first-order oracles

Reference 3

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Observation e072d9b3-9913-41f3-82f0-4167e6ed105b · outbound

This paper cites Linear coupling: An ultimate unification of gradient and mirror descent.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Linear coupling: An ultimate unification of gradient and mirror descent

Reference 4

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Observation b465f2e6-3bc9-41d6-8fa2-68a87dc66ead · outbound

This paper cites Convergence rates of inertial forward-backward algo- rithms.SIAM Journal on Optimization, 28(1):849–874, 2018.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Convergence rates of inertial forward-backward algo- rithms.SIAM Journal on Optimization, 28(1):849–874, 2018

Reference 5

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Observation 7ae6b8e2-0435-473c-a65a-10bfbfb80a26 · outbound

This paper cites Optimal convergence rates for Nesterov acceleration.SIAM Journal on Optimization, 29(4):3131–3153, 2019.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Optimal convergence rates for Nesterov acceleration.SIAM Journal on Optimization, 29(4):3131–3153, 2019

Reference 6

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Observation 03abf926-6307-40a4-bf66-0fdc7344dbc7 · outbound

This paper cites Convergence rates of the Heavy- Ball method under the lojasiewicz property.Mathematical Programming, 198(1):195–254, 2023.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Convergence rates of the Heavy- Ball method under the lojasiewicz property.Mathematical Programming, 198(1):195–254, 2023

Reference 7

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Observation 53ab85a6-738b-44f7-b02e-504280e2633a · outbound

This paper cites On the low-rank approach for semidefinite programs arising in synchronization and community detection.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization On the low-rank approach for semidefinite programs arising in synchronization and community detection

Reference 8

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Observation 9e2b02f5-f295-48c9-b860-35345e456e76 · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM journal on imaging sciences, 2(1):183–202, 2009.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM journal on imaging sciences, 2(1):183–202, 2009

Reference 9

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Observation 552c0732-1d09-4073-9d64-49524e73c203 · outbound

This paper cites Global optimality of local search for low rank matrix recovery.Advances in Neural Information Processing Systems, 29, 2016.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Global optimality of local search for low rank matrix recovery.Advances in Neural Information Processing Systems, 29, 2016

Reference 10

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Observation 781ec203-de76-4dec-bb9a-1fe0c0e94b0f · outbound

This paper cites The non-convex burer-monteiro approach works on smooth semidefinite programs.Advances in Neural Information Processing Systems, 29, 2016.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization The non-convex burer-monteiro approach works on smooth semidefinite programs.Advances in Neural Information Processing Systems, 29, 2016

Reference 11

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Observation e8305b4a-9279-41a9-a2be-b87877751405 · outbound

This paper cites A geometric alternative to Nesterov's accelerated gradient descent.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization A geometric alternative to Nesterov's accelerated gradient descent

Reference 12

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Observation e14e1c80-196f-41c4-8bb7-44d3f6790ab8 · outbound

This paper cites Phase retrieval via wirtinger flow: Theory and algorithms.IEEE Transactions on Information Theory, 61(4):1985–2007, 2015.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Phase retrieval via wirtinger flow: Theory and algorithms.IEEE Transactions on Information Theory, 61(4):1985–2007, 2015

Reference 13

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Observation cec2b605-bea3-47f2-9c80-d8b6326b9c21 · outbound

This paper cites convex until proven guilty.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization convex until proven guilty

Reference 14

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Observation 8b588e6e-17b0-41ff-845b-924ad6346204 · outbound

This paper cites Accelerated methods for nonconvex optimization.SIAM Journal on Optimization, 28(2):1751–1772, 2018.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Accelerated methods for nonconvex optimization.SIAM Journal on Optimization, 28(2):1751–1772, 2018

Reference 15

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Observation 6f1feae5-0be8-4481-9334-49335ae114fb · outbound

This paper cites Lower bounds for finding stationary points i.Mathematical Programming, 184(1-2):71–120, 2020.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Lower bounds for finding stationary points i.Mathematical Programming, 184(1-2):71–120, 2020

Reference 16

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Observation 7de991c9-e3ba-49f6-b9e7-621f1dc5fa01 · outbound

This paper cites Lower bounds for finding stationary points ii: first-order methods.Mathematical Programming, 185(1):315–355, 2021.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Lower bounds for finding stationary points ii: first-order methods.Mathematical Programming, 185(1):315–355, 2021

Reference 17

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Observation 619cfffa-7ea2-44db-9b1f-878773e6e144 · outbound

This paper cites On the convergence of the iterates of” fista”.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization On the convergence of the iterates of” fista”

Reference 18

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Observation 536d1ae5-ac3b-4b34-aed7-de8303d3624b · outbound

This paper cites The loss surfaces of multilayer networks.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization The loss surfaces of multilayer networks

Reference 19

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Observation 482ce402-e9fb-4fda-8589-861421fc6a81 · outbound

This paper cites Recent theoretical advances in non-convex optimization.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Recent theoretical advances in non-convex optimization

Reference 20

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Observation 5b321df9-850b-4e2c-998e-3be151b7eb77 · outbound

This paper cites Identifying and attacking the saddle point problem in high-dimensional non- convex optimization.Advances in neural information processing systems, 27, 2014.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Identifying and attacking the saddle point problem in high-dimensional non- convex optimization.Advances in neural information processing systems, 27, 2014

Reference 21

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Observation edd702f9-9cf7-4d41-b87f-27c866c7f112 · outbound

This paper cites Continuized accelerations of de- terministic and stochastic gradient descents, and of gossip algorithms.Advances in Neural Information Processing Systems, 34:28054–28066, 2021.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Continuized accelerations of de- terministic and stochastic gradient descents, and of gossip algorithms.Advances in Neural Information Processing Systems, 34:28054–28066, 2021

Reference 22

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Observation d2c27fb1-57ee-4618-86ee-00234a0f4b19 · outbound

This paper cites No spurious local minima in nonconvex low rank problems: A unified geometric analysis.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization No spurious local minima in nonconvex low rank problems: A unified geometric analysis

Reference 23

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Observation 38e19856-83de-49d8-a6d7-7572629a718c · outbound

This paper cites Matrix completion has no spurious local minimum.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Matrix completion has no spurious local minimum

Reference 24

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Observation 5d74ad02-27f6-4c10-a906-4447bf0e786f · outbound

This paper cites Accelerated gradient methods for nonconvex nonlinear and stochastic programming.Mathematical Programming, 156(1):59–99, 2016.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Accelerated gradient methods for nonconvex nonlinear and stochastic programming.Mathematical Programming, 156(1):59–99, 2016

Reference 25

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Observation 9e4325c9-6c77-4856-9e27-f4d094713ad3 · outbound

This paper cites Optimal first-order methods for convex functions with a quadratic upper bound.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Optimal first-order methods for convex functions with a quadratic upper bound

Reference 26

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Observation 02e3cd98-211b-4f03-8d1c-d2438598054c · outbound

This paper cites Provable non-accelerations of the heavy-ball method.arXiv preprint arXiv:2307.11291, 2023.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Provable non-accelerations of the heavy-ball method.arXiv preprint arXiv:2307.11291, 2023

Reference 27

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Observation f056417e-7bf9-4881-bd1d-c71958fa3734 · outbound

This paper cites Nesterov acceleration despite very noisy gradients.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Nesterov acceleration despite very noisy gradients

Reference 28

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Observation 510f6c1d-88ff-4b24-b1e9-a9455bcf89b2 · outbound

This paper cites Understanding alternating minimization for matrix completion.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Understanding alternating minimization for matrix completion

Reference 29

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Observation d191be63-3a75-49a3-ba52-7672b7ece26a · outbound

This paper cites Deep residual learning for image recognition.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Deep residual learning for image recognition

Reference 30

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Observation 9be398de-e694-4d90-ae7b-3c870bda253f · outbound

This paper cites Continuized nesterov acceleration for non-convex optimization.arXiv preprint arXiv:2512.16533, 2025.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Continuized nesterov acceleration for non-convex optimization.arXiv preprint arXiv:2512.16533, 2025

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Observation 4fa6b867-0c42-4289-aca6-caba65afad50 · outbound

This paper cites Study of the behaviour of nesterov accelerated gradient in a non convex setting: the strongly quasar convex case.arXiv preprint arXiv:2405.19809, 2024.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Study of the behaviour of nesterov accelerated gradient in a non convex setting: the strongly quasar convex case.arXiv preprint arXiv:2405.19809, 2024

Reference 32

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Observation ec8959bc-afe9-4882-b011-55f7b1681ba2 · outbound

This paper cites Gradient correlation is a key ingredient to accelerate sgd with momentum.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Gradient correlation is a key ingredient to accelerate sgd with momentum

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Observation 7e4f88ea-2b79-4214-9257-31eba6bd4525 · outbound

This paper cites Near-optimal methods for minimizing star- convex functions and beyond.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Near-optimal methods for minimizing star- convex functions and beyond

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source=pdf_text observed=2026-08-03T04:18:24.455882Z digest=sha256:36c8a36828de03bc986983683b8226b7951dc5647aa60e7e917206bf18ef841b

Observation 08059764-b58e-4530-bde2-cecf6c379879 · outbound

This paper cites Neural networks for machine learning.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Neural networks for machine learning

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Observation b61c3e85-2dc4-4b70-b3cc-c0cdafb86082 · outbound

This paper cites How to escape saddle points efficiently.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization How to escape saddle points efficiently

Reference 36

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source=pdf_text observed=2026-08-03T04:18:24.783436Z digest=sha256:596be282d4b6d2625830fa0d338d4f5df4cce6bbe6c4b14b72d959dc7a38fc85

Observation 17c8b51e-73b4-4de2-bedc-40be99796572 · outbound

This paper cites Accelerated gradient descent escapes saddle points faster than gradient descent.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Accelerated gradient descent escapes saddle points faster than gradient descent

Reference 37

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source=pdf_text observed=2026-08-03T04:18:24.908535Z digest=sha256:0ed32035a55a49b3ce69f63e03c6a71ba741f69b6ae089dbf6563fd213549115

Observation c3de1e1b-7595-4cda-9f91-25d0b625bd43 · outbound

This paper cites Deep learning without poor local minima.Advances in neural information processing systems, 29, 2016.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Deep learning without poor local minima.Advances in neural information processing systems, 29, 2016

Reference 38

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source=pdf_text observed=2026-08-03T04:18:25.035720Z digest=sha256:eb9efbd4743c62f0f0c9f292635954bf2ecf35f3920abbe7f0d959aff2161301

Observation ea9271d7-334f-4c38-b1bb-664e24ce5dc4 · outbound

This paper cites Kingma and Jimmy Ba.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Kingma and Jimmy Ba

Reference 39

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source=pdf_text observed=2026-08-03T04:18:25.150489Z digest=sha256:6de593187cacbd0d146dbec1e2cfe0d31f25488b55956939b0efc3263f5f7368

Observation 7c8a28fb-4720-4888-8fe3-afaead243a54 · outbound

This paper cites Gradient descent only converges to minimizers.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Gradient descent only converges to minimizers

Reference 40

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source=pdf_text observed=2026-08-03T04:18:25.317887Z digest=sha256:e2ad855de5021719b31f1a8d62e78fe822ab9b17788851db3a4e88700a8f3d66

Observation 836b96de-abf1-4ba8-9eda-2e28b202579c · outbound

This paper cites Visualizing the loss landscape of neural nets.Advances in neural information processing systems, 31, 2018.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Visualizing the loss landscape of neural nets.Advances in neural information processing systems, 31, 2018

Reference 41

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source=pdf_text observed=2026-08-03T04:18:25.409394Z digest=sha256:431287c2009068cc0fff87330afd9d06ad0659eff01bc1651d568430ddfb8192

Observation e45c0209-f975-4bfa-a331-a6e8e4ec94f9 · outbound

This paper cites Restarted nonconvex accelerated gradient descent: No more poly- logarithmic factor in the in theo(ϵ −7/4) complexity.Journal of Machine Learning Research, 24(157):1–37, 2023.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Restarted nonconvex accelerated gradient descent: No more poly- logarithmic factor in the in theo(ϵ −7/4) complexity.Journal of Machine Learning Research, 24(157):1–37, 2023

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source=pdf_text observed=2026-08-03T04:18:25.515994Z digest=sha256:6d0c6a12d83b2d4fa15933ac319fa36d2fa26d58daaf101902175c72abf06b7d

Observation 1eb96575-0b04-4d64-ba9c-9f55a01d86e2 · outbound

This paper cites Parameter-free accelerated gradient descent for nonconvex minimization.SIAM J.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Parameter-free accelerated gradient descent for nonconvex minimization.SIAM J

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source=pdf_text observed=2026-08-03T04:18:25.595119Z digest=sha256:b7d6a111c629b12197542e2c0d88fa6f8ffd530d8803f2a7cb1575b8cd756bcd

Observation e07d60e3-7478-485a-b28d-1c1edcc09dc9 · outbound

This paper cites Universal heavy-ball method for nonconvex optimization under h¨ older continuous hessians.Mathematical Programming, 212(1):147–175, 2025.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Universal heavy-ball method for nonconvex optimization under h¨ older continuous hessians.Mathematical Programming, 212(1):147–175, 2025

Reference 44

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source=pdf_text observed=2026-08-03T04:18:25.654900Z digest=sha256:4d6941d086437625f5d463d82366147eb093064741228cdf07e0edb6f4aa4028

Observation b5f49cd6-f2ab-4d49-b033-832544a61935 · outbound

This paper cites Solving sdps for synchronization and maxcut problems via the grothendieck inequality.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Solving sdps for synchronization and maxcut problems via the grothendieck inequality

Reference 45

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source=pdf_text observed=2026-08-03T04:18:25.732444Z digest=sha256:ab42c3d9c5c92c0184f401871022a8ae8cfcf67b49b4f5dac8441b42b314eba4

Observation e48ff65a-ee5d-441e-8e2b-57d28aeed58d · outbound

This paper cites A 2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning.Advances in Neural Information Processing Systems, 36:47451–47474, 2023.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization A 2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning.Advances in Neural Information Processing Systems, 36:47451–47474, 2023

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source=pdf_text observed=2026-08-03T04:18:25.848618Z digest=sha256:3fc34c2be5b5a843853581f0451be7cb6673346972f5c96cff2d7535ea498ac5

Observation fa3434c9-caa7-41da-b54d-838d2b3d71d5 · outbound

This paper cites Dadao: Decoupled accelerated decentralized asynchronous optimization.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Dadao: Decoupled accelerated decentralized asynchronous optimization

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source=pdf_text observed=2026-08-03T04:18:25.958517Z digest=sha256:3469fcc6ce93fe2dfb2393c0398c35395d9c5fbb93a22b5a843fdd60ae4965d1

Observation dbb873a5-a746-409b-9608-7af9947416e6 · outbound

This paper cites Problem complexity and method efficiency in optimization.Wiley-Interscience, 1983.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Problem complexity and method efficiency in optimization.Wiley-Interscience, 1983

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source=pdf_text observed=2026-08-03T04:18:26.039935Z digest=sha256:56323260f8290ea8c786ddbc9c888a766f56f4802a4c591583cf4ef1f9775863

Observation 97f85315-4fdb-4e40-9f54-500e78cfa9d7 · outbound

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

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization A method for solving the convex programming problem with convergence rate o(1/k2)

Reference 49

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source=pdf_text observed=2026-08-03T04:18:26.120666Z digest=sha256:cdf7b9508cd54c580b472a3d5c43c77764e0efaaa8db0f14f1ced9e3795d5840

Observation 6119e3cd-d188-4e5e-9d5f-428589bd7ffb · outbound

This paper cites Introductory lectures on convex optimization.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Introductory lectures on convex optimization

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source=pdf_text observed=2026-08-03T04:18:26.178448Z digest=sha256:dab92abf3396e8829899b5c0f3c54630de5ca58c7509d7f12fd6d3bacbbcf886

Observation 361b86c0-1af8-48d7-a617-42b9a55e2640 · outbound

This paper cites Springer, 2018.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Springer, 2018

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source=pdf_text observed=2026-08-03T04:18:26.292086Z digest=sha256:face97c6072638974ac3404836f023227b51c06e2a508bf57bb760de99e20893

Observation 7368506e-b384-44a3-82db-f12fee673a18 · outbound

This paper cites Cubic regularization of newton method and its global performance.Mathematical programming, 108(1):177–205, 2006.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Cubic regularization of newton method and its global performance.Mathematical programming, 108(1):177–205, 2006

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source=pdf_text observed=2026-08-03T04:18:26.375112Z digest=sha256:fbb328785557b25b4008088343a2a57923233d22dbd6a442680483e7ac1901d4

Observation d94d8524-44bf-4e98-895c-39fea57c23b0 · outbound

This paper cites Heavy-ball Differential Equation Achieves $O(\varepsilon^{-7/4})$ Convergence for Nonconvex Functions.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Heavy-ball Differential Equation Achieves $O(\varepsilon^{-7/4})$ Convergence for Nonconvex Functions

Reference 53

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source=pdf_text observed=2026-08-03T04:18:26.438378Z digest=sha256:a7f5a9139ff6014da78bd83d37b58a2b9370506c60803a3c3c04363ab37dcdb5

Observation 0417125f-177a-409a-80f6-79bd9f5d94fe · outbound

This paper cites Behavior of accelerated gradient methods near critical points of nonconvex functions.Mathematical Programming, 176(1):403–427, 2019.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Behavior of accelerated gradient methods near critical points of nonconvex functions.Mathematical Programming, 176(1):403–427, 2019

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source=pdf_text observed=2026-08-03T04:18:26.513123Z digest=sha256:c085367a79375c09d53ee150228417be999f75b25670ce6d03424ce13b5007b8

Observation d04926ce-db24-4e9c-b7d4-89860e648f9f · outbound

This paper cites Some methods of speeding up the convergence of iteration methods.USSR Computational Mathematics and Mathematical Physics, 4(5):1–17, 1964.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Some methods of speeding up the convergence of iteration methods.USSR Computational Mathematics and Mathematical Physics, 4(5):1–17, 1964

Reference 55

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source=pdf_text observed=2026-08-03T04:18:26.571664Z digest=sha256:9cbc22444e24ab59008675981f5bbfd746fe5e12453bd1ea4efe3a79ceeaf921

Observation 00989b78-82ed-4854-92ad-4a42648c6af2 · outbound

This paper cites Stochastic differential equations.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Stochastic differential equations

Reference 56

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source=pdf_text observed=2026-08-03T04:18:26.676560Z digest=sha256:b4a00d58b64444d43425c59ce2034a552363f078794197d5559d3bfa4664b28a

Observation 61ee4e4d-fdb5-4be7-b98c-b4f8a864043a · outbound

This paper cites Provably accelerated imaging with restarted inertia and score-based image priors.arXiv preprint arXiv:2510.07470, 2025.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Provably accelerated imaging with restarted inertia and score-based image priors.arXiv preprint arXiv:2510.07470, 2025

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source=pdf_text observed=2026-08-03T04:18:26.756514Z digest=sha256:09b6b83a63169236aaed5a7765b48ca24bc920a57a9147390969eac750061186

Observation b4757eff-3ede-4240-babc-22959e6ea703 · outbound

This paper cites Accelerated First-Order Methods: Differential Equations and Lyapunov Functions.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Accelerated First-Order Methods: Differential Equations and Lyapunov Functions

Reference 58

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source=pdf_text observed=2026-08-03T04:18:26.839165Z digest=sha256:6af2255de51e0a279a89dd4241de838b4a0b7a0cc5acb8dd6cb7a236562bcc8d

Observation b0c72b82-f107-4348-b6bb-6cbfd75f157f · outbound

This paper cites Cand` es.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Cand` es

Reference 59

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source=pdf_text observed=2026-08-03T04:18:26.922794Z digest=sha256:1938085dd67bf6501514cd9de47956e13e72ee55e52da88bcd6c52428ffeca9d

Observation 7509a7be-c704-4444-8c8e-2dac027202b9 · outbound

This paper cites Heavy-ball Algorithms Always Escape Saddle Points.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Heavy-ball Algorithms Always Escape Saddle Points

Reference 60

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source=pdf_text observed=2026-08-03T04:18:27.004843Z digest=sha256:ff6cb7678f4cc484d94090e86f0c542016fa3b814a974ce6a4869e842fa3910e

Observation deca3bcb-91e7-4501-85f1-ad85b97baf20 · outbound

This paper cites On the importance of ini- tialization and momentum in deep learning.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization On the importance of ini- tialization and momentum in deep learning

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source=pdf_text observed=2026-08-03T04:18:27.086577Z digest=sha256:89027705b00fc17900590a1f3ae28f598deb99451bfd16f27366b8e984171561

Observation 8cde4f36-f88a-4e9f-99a7-37124b914617 · outbound

This paper cites Continuized acceleration for quasar convex functions in non-convex optimization, 2023.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Continuized acceleration for quasar convex functions in non-convex optimization, 2023

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source=pdf_text observed=2026-08-03T04:18:27.152191Z digest=sha256:fb4b0f1369b14359bad842d3a9b855794228332689e21340657b470c001c9767

Observation 400d23ad-9f62-4e11-a654-adadba4a5abb · outbound

This paper cites First-order stochastic algorithms for escaping from saddle points in almost linear time.Advances in neural information processing systems, 31, 2018.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization First-order stochastic algorithms for escaping from saddle points in almost linear time.Advances in neural information processing systems, 31, 2018

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source=pdf_text observed=2026-08-03T04:18:27.189656Z digest=sha256:7a0400052ae30b3c8e1faef63a2ce22fc7902f476a71b6adc2bb8f1f49513188

Observation 5d8cc8cd-903e-46c5-ab11-c98d0795a4f9 · outbound

This paper cites Z (Ti,Ti+1) η2 ∥zs −x s∥2 ds # E[A i+1] ≤Cmax i∈{1,...,n−1} E[A i+1] n−1X i=1 E.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Z (Ti,Ti+1) η2 ∥zs −x s∥2 ds # E[A i+1] ≤Cmax i∈{1,...,n−1} E[A i+1] n−1X i=1 E

Reference 64

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source=pdf_text observed=2026-08-03T04:18:27.266442Z digest=sha256:5565cbaf57a62141840c8b9ca527eca486be1c9cb262f9c29c8f66ccdd88e43b

Observation 2e4e024d-e39d-47fd-943e-71c65452865a · outbound

This paper cites Z Tn 0 Λt wt(t)(zt −x t) γ′ −γ dNt # =E.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Z Tn 0 Λt wt(t)(zt −x t) γ′ −γ dNt # =E

Reference 65

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source=pdf_text observed=2026-08-03T04:18:27.323597Z digest=sha256:0d457c66f10743c6b9c237fb260d1f1077ec19571a3ded7bd87874208451048b

Observation ca048ee4-0c52-4e19-818e-96c0982a979e · outbound

This paper cites an unresolved cited work.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization Unresolved cited work

Reference 66

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source=pdf_text observed=2026-08-03T04:18:27.434112Z digest=sha256:a8a554a163d4f395af1646292bd854aebd76d7219686b3a163c94ce47cb7812b

Observation bbfc1373-70d9-4b88-958d-9b6979f29037 · outbound

This paper cites 1An α2L2 Z Tn 0 η2 ∥zs −x s∥2 Z Tn s Z t s Z s 0 eα(τ−t) eα(σ−t)(τ−σ)dN σdNτ dNt ! ds # ≤CL 2 (1 + 2α)4 α2 E.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization 1An α2L2 Z Tn 0 η2 ∥zs −x s∥2 Z Tn s Z t s Z s 0 eα(τ−t) eα(σ−t)(τ−σ)dN σdNτ dNt ! ds # ≤CL 2 (1 + 2α)4 α2 E

Reference 67

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source=pdf_text observed=2026-08-03T04:18:27.549160Z digest=sha256:93822a613db142222b3f78dbc76ecdf545af67920ca7f9595aaa10b17d228ed7

Observation f5952b3d-f483-40b1-8285-b572d3faa3c6 · outbound

This paper cites 1An α2L2 Z Tn 0 γ2 ∥∇f(x s− )∥2 Z Tn s Z t s Z s 0 eα(τ−t) eα(σ−t)(Nτ − −N σ− )dNσdNτ dNt ! dNs # ≤Cα −1L2 (1 + 2α)5 α4 E.

Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization 1An α2L2 Z Tn 0 γ2 ∥∇f(x s− )∥2 Z Tn s Z t s Z s 0 eα(τ−t) eα(σ−t)(Nτ − −N σ− )dNσdNτ dNt ! dNs # ≤Cα −1L2 (1 + 2α)5 α4 E

Reference 68

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Accelerated Stochastic Zeroth-Order Quasar-Convex Optimization cites this paper.

Accelerated Stochastic Zeroth-Order Quasar-Convex Optimization Continuized Nesterov Momentum Achieves the $O(\varepsilon^{-7/4})$ Complexity in Smooth Nonconvex Optimization

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