Paper Citation Record · LEDGER

Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2306.12418.

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

pith.paper-citation-record.v1
2306.12418 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:20:55.809995Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:46:15.469688Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 09d9cad9-1842-485f-846e-a075523d28ab · inbound

Randomized coupled decompositions cites this paper.

Randomized coupled decompositions Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:53:18.637498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6ed05901-d94c-4631-8f7f-20c0f9f0cc46 · inbound

What is a Sketch-and-Precondition Derivation for Low-Rank Approximation? Inverse Power Error or Inverse Power Estimation? cites this paper.

What is a Sketch-and-Precondition Derivation for Low-Rank Approximation? Inverse Power Error or Inverse Power Estimation? Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-08T11:20:55.809995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8ce8ebe-d4e0-4f25-aabd-6246565daec7 · inbound

Quasi-optimal hierarchically semi-separable matrix approximation cites this paper.

Quasi-optimal hierarchically semi-separable matrix approximation Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:40.982710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5485c390-40e7-4350-b1a0-df665649b8c4 · inbound

Accelerating Large-Scale Regularized High-Order Tensor Recovery cites this paper.

Accelerating Large-Scale Regularized High-Order Tensor Recovery Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:10.176993Z

Source-reported events for the cited work

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Observation 64812ccd-1929-4ced-bd92-1c66bd1ececb · inbound

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques cites this paper.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:20.731323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83ff9243-182a-4122-9610-19b653e93574 · inbound

A structural bound for cluster robustness of randomized small-block Lanczos cites this paper.

A structural bound for cluster robustness of randomized small-block Lanczos Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:50:30.636603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5eb23d-054b-4207-b01b-fcbdd9843182 · inbound

Tomography-assisted noisy quantum circuit simulator using matrix product density operators cites this paper.

Tomography-assisted noisy quantum circuit simulator using matrix product density operators Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T22:05:28.678017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cea9e27-cb33-4145-a652-d32ddbf44bc9 · inbound

Faster Linear Algebra Algorithms with Structured Random Matrices cites this paper.

Faster Linear Algebra Algorithms with Structured Random Matrices Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-05T14:36:57.741732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1819cbb4-99d4-44a5-9b3c-785e0d5d20a1 · inbound

The Filter Echo: A General Tool for Filter Visualisation cites this paper.

The Filter Echo: A General Tool for Filter Visualisation Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:26:36.989809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97bf826c-ec0c-4031-8234-8dc6973d2efc · inbound

How many integrals should be evaluated at least in two-dimensional hyperinterpolation? cites this paper.

How many integrals should be evaluated at least in two-dimensional hyperinterpolation? Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T09:50:55.425680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a6f076f-d914-4bae-b0a2-486357892dce · inbound

Approximate full conformal prediction in an RKHS cites this paper.

Approximate full conformal prediction in an RKHS Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T09:48:03.204829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc01ec1-b9ad-4d68-8f57-e115bcc2960e · inbound

Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration cites this paper.

Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:53:16.004196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 06e63e7d-1da6-4401-8a9b-5d68518ba183 · inbound

Fast and accurate conditioning for large-scale Gaussian process prediction problems cites this paper.

Fast and accurate conditioning for large-scale Gaussian process prediction problems Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:06:17.024635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0c8fcacb-79b1-44d7-b114-e1bc5c41d1c4 · inbound

Fast and accurate conditioning for large-scale Gaussian process prediction problems cites this paper.

Fast and accurate conditioning for large-scale Gaussian process prediction problems Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:45:12.318376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6823186-4519-429b-92a5-f83b5fb4b14e · inbound

Finding accurate eigenvalues and eigenvectors of positive semi-definite matrices given a subspace cites this paper.

Finding accurate eigenvalues and eigenvectors of positive semi-definite matrices given a subspace Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:16:08.739011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a3011b8-afd7-4528-8069-2bedc824b3ac · inbound

Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation cites this paper.

Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.749039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1683779f-9e34-4b3d-ae7f-c29832612776 · inbound

Random Matrix Spectra from Boltzmann-Weighted Lattice Ensembles cites this paper.

Random Matrix Spectra from Boltzmann-Weighted Lattice Ensembles Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:43:56.236346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eda58f3f-9c68-4612-81a7-1dfdb077a1da · inbound

CSULoRA: Closest Safe Update Low-Rank Adaptation cites this paper.

CSULoRA: Closest Safe Update Low-Rank Adaptation Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T08:33:15.752463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1d7a2d85-c425-4327-b27e-5027d63e1ed8 · inbound

Transpose-free linear algebra cites this paper.

Transpose-free linear algebra Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:46:15.471373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2d9c1b38-1f4f-4252-bb08-e4215beb2027 · inbound

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation cites this paper.

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:06:14.408274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 777d26ef-0868-4b50-9802-d7463420a809 · inbound

Intrinsic Low-Tucker-Rank Theory and Unified Tensor CUR Decomposition for High-Dimensional Hyperinterpolation cites this paper.

Intrinsic Low-Tucker-Rank Theory and Unified Tensor CUR Decomposition for High-Dimensional Hyperinterpolation Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T11:56:57.823398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2baa3960-36fb-45b3-82cd-324ecf295877 · inbound

Reduced Basis Method for Simulating Thermal Transients in Electric Machines cites this paper.

Reduced Basis Method for Simulating Thermal Transients in Electric Machines Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T21:28:56.738119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.