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Paper Citation Record · LEDGER

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure

As of 10 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.11724.

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pith.paper-citation-record.v1
2507.11724 v1

Coverage vector

measured 62 of 62 reference resolution

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measured 62 of 62 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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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External citation measurements

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Outbound references

Observation f0433b6a-ab8e-49b4-a832-9ee4539a0d0e · outbound

This paper cites Leverage score sampling for faster accelerated regression and erm.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Leverage score sampling for faster accelerated regression and erm

Reference 1

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Observation 95ecb02d-82f0-4e78-ad79-f4cdf3a9e198 · outbound

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

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Katyusha: The first direct acceleration of stochastic gradient methods

Reference 2

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Observation 4d5a5dfa-439b-457f-b5ac-ea1f8da0fe7f · outbound

This paper cites More asymmetry yields faster matrix multiplication.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure More asymmetry yields faster matrix multiplication

Reference 3

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Observation 9652fba1-4158-4b4b-9b1f-8e4cdfa15de9 · outbound

This paper cites Faster kernel ridge regression using sketching and preconditioning.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Faster kernel ridge regression using sketching and preconditioning

Reference 4

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Observation cb65c687-5726-4341-995e-c98fcb8a23ab · outbound

This paper cites On the rate of convergence of the preconditioned conjugate gradient method.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure On the rate of convergence of the preconditioned conjugate gradient method

Reference 5

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Observation 6b136cb3-a35d-49aa-a1d4-36a4f93e91de · outbound

This paper cites Optimal oblivious subspace embeddings with near-optimal sparsity.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Optimal oblivious subspace embeddings with near-optimal sparsity

Reference 6

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Observation 951fe63b-8c67-4fff-89f9-66c627af514f · outbound

This paper cites Optimal embedding dimension for sparse subspace embeddings.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Optimal embedding dimension for sparse subspace embeddings

Reference 7

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Source-reported events for the cited work

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Observation 0a7cb02c-6600-463b-a857-9cb59312914d · outbound

This paper cites Clarkson and David P.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Clarkson and David P

Reference 8

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Source-reported events for the cited work

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Observation 08edd9b0-a319-4ca0-97ec-c3b6bf73efef · outbound

This paper cites Nearly tight oblivious subspace embeddings by trace inequalities.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nearly tight oblivious subspace embeddings by trace inequalities

Reference 9

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Observation bdb770af-c4ce-4fe9-b993-d8e1daed7622 · outbound

This paper cites Solving directed laplacian systems in nearly-linear time through sparse lu factorizations.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Solving directed laplacian systems in nearly-linear time through sparse lu factorizations

Reference 10

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Observation c943cbc6-d3af-47c6-9048-bdcbf64b4cff · outbound

This paper cites Cohen, Jonathan A.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Cohen, Jonathan A

Reference 11

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Observation 3babb9ee-464d-4d26-8c09-c6e8a4b7260e · outbound

This paper cites Cohen, Rasmus Kyng, Gary L.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Cohen, Rasmus Kyng, Gary L

Reference 12

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Source-reported events for the cited work

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Observation 04c12a4c-42fc-4156-966c-4e4e4603a12f · outbound

This paper cites Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, and Aaron Sidford.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, and Aaron Sidford

Reference 13

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Source-reported events for the cited work

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Observation 79f274e0-96f1-413b-81a0-b4e60902b536 · outbound

This paper cites Optimal approximate matrix product in terms of stable rank.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Optimal approximate matrix product in terms of stable rank

Reference 14

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Observation f389bd51-ae35-4784-9882-decd35dd6357 · outbound

This paper cites Matrix multiplication via arithmetic progressions.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Matrix multiplication via arithmetic progressions

Reference 15

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Observation 842ff77a-6f31-4ffc-9402-883ad0ae3413 · outbound

This paper cites Fast linear algebra is stable.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Fast linear algebra is stable

Reference 16

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Source-reported events for the cited work

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Observation 824a91fb-0045-4514-a4f6-0802691dc222 · outbound

This paper cites Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems

Reference 17

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Source-reported events for the cited work

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Observation da285e59-688d-409d-8f6a-cd0030301fae · outbound

This paper cites Faster linear systems and matrix norm approximation via multi-level sketched preconditioning.ACM-SIAM Symposium on Discrete Algorithms (SODA), 2025.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Faster linear systems and matrix norm approximation via multi-level sketched preconditioning.ACM-SIAM Symposium on Discrete Algorithms (SODA), 2025

Reference 18

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Observation 0253e6df-d79a-4172-b0a0-81aea3809f53 · outbound

This paper cites Randomized Kaczmarz Methods with Beyond-Krylov Convergence.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Randomized Kaczmarz Methods with Beyond-Krylov Convergence

Reference 19

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Source-reported events for the cited work

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Observation 98f9c2b5-4aba-4fac-8ad2-61c10a617b0c · outbound

This paper cites Solving linear systems faster than via preconditioning.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Solving linear systems faster than via preconditioning

Reference 20

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Observation 17f16d86-011e-41da-bf73-c69928a600a6 · outbound

This paper cites Randomized Nystr\"om Preconditioning.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Randomized Nystr\"om Preconditioning

Reference 21

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Observation 105a8e24-a6f7-4601-8130-87d044f4e5f2 · outbound

This paper cites Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

Reference 22

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Observation 8aac9b4c-3a70-47c7-b829-39e2ed7b7f09 · outbound

This paper cites Principal component projection without principal component analysis.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Principal component projection without principal component analysis

Reference 23

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Observation d7906b21-f634-4ea5-b8bd-2a6d418d2999 · outbound

This paper cites Faster eigenvector computation via shift-and-invert preconditioning.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Faster eigenvector computation via shift-and-invert preconditioning

Reference 24

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Observation c3725bf0-4329-4095-8b1a-334271db75a8 · outbound

This paper cites Matrix computations.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Matrix computations

Reference 25

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Source-reported events for the cited work

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Observation 36a69766-0b68-47a9-ac32-08ee7a04c24f · outbound

This paper cites Chebyshev semi-iterative methods, successive overrelaxation iterative methods, and second order richardson iterative methods.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Chebyshev semi-iterative methods, successive overrelaxation iterative methods, and second order richardson iterative methods

Reference 26

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Source-reported events for the cited work

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

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Observation 79266890-e2bc-4654-af7b-d84553ff6d60 · outbound

This paper cites Solving ridge regression using sketched preconditioned svrg.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Solving ridge regression using sketched preconditioned svrg

Reference 27

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Source-reported events for the cited work

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Observation 52a47818-61b7-4e29-b61f-d1b1730006f2 · outbound

This paper cites Behavior of slightly perturbed lanczos and conjugate-gradient recurrences.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Behavior of slightly perturbed lanczos and conjugate-gradient recurrences

Reference 28

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Source-reported events for the cited work

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Observation 2fc8c636-502b-47a8-acfd-a552844de10f · outbound

This paper cites Methods of conjugate gradients for solving linear systems, volume 49.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Methods of conjugate gradients for solving linear systems, volume 49

Reference 29

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Source-reported events for the cited work

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Observation 7a784a8b-2d8b-4ba6-a2e6-88d537b19224 · outbound

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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Gaussian elimination

Reference 30

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Source-reported events for the cited work

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Observation 593fd0b7-7453-4a90-899a-658a0558e6bf · outbound

This paper cites Structured semidefinite programming for recovering structured preconditioners.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Structured semidefinite programming for recovering structured preconditioners

Reference 31

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Source-reported events for the cited work

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Observation 51155ddd-b9db-48ef-b806-9ac064107f7a · outbound

This paper cites Ultrasparse ultrasparsifiers and faster laplacian system solvers.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Ultrasparse ultrasparsifiers and faster laplacian system solvers

Reference 32

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Source-reported events for the cited work

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Observation 9722e25b-aa4c-43ba-8910-57ee61b43627 · outbound

This paper cites Accelerating stochastic gradient descent using predictive variance reduc- tion.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Accelerating stochastic gradient descent using predictive variance reduc- tion

Reference 33

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Source-reported events for the cited work

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

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Observation a96e8c41-c8a8-4aaf-98e4-a12f8fe8d02f · outbound

This paper cites Single pass spectral sparsification in dynamic streams.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Single pass spectral sparsification in dynamic streams

Reference 34

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Source-reported events for the cited work

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

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Observation f4886cd3-4875-4356-bdba-c8e6c81281ac · outbound

This paper cites Miller, and Richard Peng.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Miller, and Richard Peng

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.372342Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:10.696391Z digest=sha256:1ae12cca16f834ae271bbce2f27626b69670d08a9264913d1c010d237c00cf24

Observation 13d3ff89-7737-43c8-a75c-74dc558bb645 · outbound

This paper cites Miller, and Richard Peng.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Miller, and Richard Peng

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.362268Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:10.771379Z digest=sha256:e9995fb5a98730fc1a428286ec9dbeb73a0565fbdb187f3a5b5be2a44e19038a

Observation 137c1f73-8156-4245-a20e-9b046798c64b · outbound

This paper cites Estimating the largest eigenvalue by the power and lanczos algorithms with a random start.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Estimating the largest eigenvalue by the power and lanczos algorithms with a random start

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.352517Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:10.915412Z digest=sha256:46136d891912f6122881f224ec8a4299471ae8e287e5c337ed95c60dbcc018d9

Observation 2567b221-bfac-44e6-abab-63ab424a0450 · outbound

This paper cites Sparsified cholesky and multigrid solvers for connection laplacians.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Sparsified cholesky and multigrid solvers for connection laplacians

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.343590Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:10.995362Z digest=sha256:838469540e401a41116c092291d69984d1762581355ba9958b0e1807e5652877

Observation 07ec86cc-c84d-4273-b781-ee0933092059 · outbound

This paper cites Approximate gaussian elimination for laplacians-fast, sparse, and simple.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Approximate gaussian elimination for laplacians-fast, sparse, and simple

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T17:14:11.078840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:14:11.078840Z digest=sha256:81fdd247335788d3fe7f5a0a10c2edb804e19d5347d47655d211578e929a36f8

Observation ab0a6b2c-085f-4e27-86a1-f0974fa1a949 · outbound

This paper cites Faster algorithms for rectangular matrix multiplication.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Faster algorithms for rectangular matrix multiplication

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.268781Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.154837Z digest=sha256:bf0f76b4510bd5b9cbc4a40125f29185308b079eafe2c5fc0491dae6680c9098

Observation 3d327eb1-773e-4e1d-864f-92b89a962a98 · outbound

This paper cites Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.169771Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.235991Z digest=sha256:e7b3887f7c3e634044ae548fbb919ebb0fa5a096b3bdb71db32b0ee644e46611

Observation 3195aa0d-c5a7-404d-ac63-e211aa61b7e1 · outbound

This paper cites Randomized methods for linear constraints: convergence rates and conditioning.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Randomized methods for linear constraints: convergence rates and conditioning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:16.056494Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.342674Z digest=sha256:0173d2a30be5016c9408dd23d737faaa61cef26065851a066862e5103205dc40

Observation 261ea882-66f7-48c3-9629-7f473443248c · outbound

This paper cites Miller, and Richard Peng.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Miller, and Richard Peng

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:15.951501Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.394863Z digest=sha256:94c400bc21ad22f5e779d9d9d5cf975fe88da2d8285ab8842363c37a77c2e7d1

Observation 549893fa-7b24-4991-aeab-fb5b757956dc · outbound

This paper cites A universal catalyst for first-order optimization.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A universal catalyst for first-order optimization

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:15.853314Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.474242Z digest=sha256:8d573c3098d5eb7808d36bff91efd01285bc0c06a06388442a2cbcd51b1d9922

Observation 6197efbe-74b2-4564-b368-a27f953498c1 · outbound

This paper cites Stability of the lanczos method for matrix function approximation.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Stability of the lanczos method for matrix function approximation

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:15.832308Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.559460Z digest=sha256:4166450cf90953a91699d00726744e79d5f5b043efe98968055aeffdc05242fe

Observation 5f78be97-236f-4a15-9bc5-5559ff960d02 · outbound

This paper cites Spec- trum approximation beyond fast matrix multiplication: Algorithms and hardness.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Spec- trum approximation beyond fast matrix multiplication: Algorithms and hardness

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:15.558071Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.677329Z digest=sha256:3907462645949274799907cc9f2f6c679d011a723420c063d8a6adb1a4d02a7c

Observation 9888734c-0138-41f1-85f6-ec9baceb40f5 · outbound

This paper cites Nguyˆ en.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nguyˆ en

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:15.204990Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.746056Z digest=sha256:21416e89eb0041e09199a61a273c4dc3c41417bb759d0a8968fd4243e1b775f2

Observation 44064436-770d-4fbd-af3b-56d0ea4378c3 · outbound

This paper cites Nesterov.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nesterov

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.786532Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.811393Z digest=sha256:e5de6c8a8074eb2b8195a4fe01de80b70f7c17510baa0dee988053931c443ab4

Observation 87956472-9c75-451c-99f3-f1ca89ced0cb · outbound

This paper cites Nesterov and Sebastian U.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nesterov and Sebastian U

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.610650Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.891653Z digest=sha256:47c15db0fc747dcb40334dddd1998f412b88da87d130c96e2a03f3357c6e7833

Observation 57d74947-e8e2-418b-9612-d910515ef30a · outbound

This paper cites Algorithm Design Using Spectral Graph Theory.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Algorithm Design Using Spectral Graph Theory

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.602882Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:11.977772Z digest=sha256:67a3330e4ba5acf4cc9d90e56e737064a4ad5deda8f0655047362ec1eafc44be

Observation e3e7bb26-521d-46a0-abb7-2a798edc611c · outbound

This paper cites Sparsified block elimination for directed laplacians.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Sparsified block elimination for directed laplacians

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.487937Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.058571Z digest=sha256:75e7a779533ea236dd62cedaa296052556561136575bf478b9535f909b55d136

Observation ad560075-89c7-4079-9c36-d7c571c4be6e · outbound

This paper cites A fast randomized algorithm for overdetermined linear least-squares regression.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A fast randomized algorithm for overdetermined linear least-squares regression

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.362972Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.135900Z digest=sha256:0e7b653da3b687d45e546b1d44e29fa00607bf648c4a32b0ca29243bbfc562de

Observation 8697bbad-e85a-4d0a-bee8-9dc5c7aa9520 · outbound

This paper cites Improved approximation algorithms for large matrices via random projections.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Improved approximation algorithms for large matrices via random projections

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.241662Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.201670Z digest=sha256:1a0ca2a7d6b2409d4e49cba7f3ab821011133b5c48ed7ce9811e5cfb04a57b0b

Observation 1b5d6705-7063-4a78-af65-ce851d93388f · outbound

This paper cites Spielman and Shang-Hua Teng.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Spielman and Shang-Hua Teng

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.118677Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.296345Z digest=sha256:7f986a6473eb70d7590be6b432ef0460ec2a5272dbc55ac7f585c15431fa26be

Observation 42f5b7ea-4975-4a56-ba37-9dc530329552 · outbound

This paper cites A Note on Preconditioning by Low-Stretch Spanning Trees.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A Note on Preconditioning by Low-Stretch Spanning Trees

Reference 55

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:14:13.025774Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.368816Z digest=sha256:007b2b0e71878aedd56aac652bfdb190836cc4b93d958eb491d75e612177fcac

Observation 2a5e4bb7-303b-4e6e-9c60-67812776a5cc · outbound

This paper cites Gaussian elimination is not optimal.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Gaussian elimination is not optimal

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:14.014602Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.431613Z digest=sha256:c8a8e5f36046682c39c0321d655a5d4d76b78efd77ac683a10bc6908f7f547c1

Observation b7977877-dbe5-4e73-bb53-f6fe9d71201f · outbound

This paper cites A randomized Kaczmarz algorithm with exponential conver- gence.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A randomized Kaczmarz algorithm with exponential conver- gence

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:13.873479Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.505523Z digest=sha256:266b6a6c4957fd5f97ffd185002586192ecff9febbb54a44ed7f99aa8fdd9b2d

Observation 963f8dbd-6a6e-411d-8ad2-4806c5cc0549 · outbound

This paper cites an unresolved cited work.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-06T17:14:13.766112Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.568367Z digest=sha256:7b505988e41121b82cadbbc07fe56a05b79cde9cfb5bd17ccec5e475720aac27

Observation 72b19926-b385-4f84-98d8-3a1101d4f5de · outbound

This paper cites Multiplying matrices faster than coppersmith-winograd.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Multiplying matrices faster than coppersmith-winograd

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:13.650856Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.661168Z digest=sha256:636edf2f81c8a8a3b06b0f73d547f0558b88dd3cc20a2f5f5ad38d2753ce0389

Observation 09be5b42-666d-486d-bb0d-5f42c17416a3 · outbound

This paper cites New bounds for matrix multiplication: from alpha to omega.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure New bounds for matrix multiplication: from alpha to omega

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:13.539211Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.730293Z digest=sha256:65a9292c0b1f43e7214c4d52a2ae0f7b4885d88ca47dbbb8fbe701ddf1ef06d0

Observation 799ca7c4-7247-44e6-9133-c9e8feda4dbe · outbound

This paper cites Sketching as a tool for numerical linear algebra.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Sketching as a tool for numerical linear algebra

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:13.410680Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.808069Z digest=sha256:7e69d6d3145c4b6c45293f72bf1e7f999dc3fc3c830baf309aed56723ee4ed51

Observation c48e1435-0088-47b4-870f-ce2c8a51d85d · outbound

This paper cites Even faster accelerated coordinate descent using non-uniform sampling.

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Even faster accelerated coordinate descent using non-uniform sampling

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:14:13.329587Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T17:14:12.917160Z digest=sha256:b2fe52ef38f1f3e5e5aa0534f01e95bba10eb2ee20b4333d47d55e672dbe0dd4

Pith citing papers

No inbound Pith citation observations are available.