Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:14:12.917160Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.11724.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:14:12.917160Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
62 of 62 outbound references displayed
External citation measurements
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Observation f0433b6a-ab8e-49b4-a832-9ee4539a0d0e · outbound
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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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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure More asymmetry yields faster matrix multiplication
Reference 3
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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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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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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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Optimal embedding dimension for sparse subspace embeddings
Reference 7
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Clarkson and David P
Reference 8
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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
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
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
Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Cohen, Rasmus Kyng, Gary L
Reference 12
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Observation 04c12a4c-42fc-4156-966c-4e4e4603a12f · outbound
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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Observation 79f274e0-96f1-413b-81a0-b4e60902b536 · outbound
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
Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Matrix multiplication via arithmetic progressions
Reference 15
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Fast linear algebra is stable
Reference 16
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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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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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Randomized Kaczmarz Methods with Beyond-Krylov Convergence
Reference 19
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Observation 98f9c2b5-4aba-4fac-8ad2-61c10a617b0c · outbound
Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Solving linear systems faster than via preconditioning
Reference 20
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Randomized Nystr\"om Preconditioning
Reference 21
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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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Principal component projection without principal component analysis
Reference 23
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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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Matrix computations
Reference 25
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Observation 36a69766-0b68-47a9-ac32-08ee7a04c24f · outbound
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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Observation 79266890-e2bc-4654-af7b-d84553ff6d60 · outbound
Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Solving ridge regression using sketched preconditioned svrg
Reference 27
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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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Observation 2fc8c636-502b-47a8-acfd-a552844de10f · outbound
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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Gaussian elimination
Reference 30
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Structured semidefinite programming for recovering structured preconditioners
Reference 31
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Ultrasparse ultrasparsifiers and faster laplacian system solvers
Reference 32
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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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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Single pass spectral sparsification in dynamic streams
Reference 34
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Miller, and Richard Peng
Reference 35
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Miller, and Richard Peng
Reference 36
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Observation 137c1f73-8156-4245-a20e-9b046798c64b · outbound
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
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Sparsified cholesky and multigrid solvers for connection laplacians
Reference 38
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Approximate gaussian elimination for laplacians-fast, sparse, and simple
Reference 39
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Faster algorithms for rectangular matrix multiplication
Reference 40
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Observation 3d327eb1-773e-4e1d-864f-92b89a962a98 · outbound
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
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Randomized methods for linear constraints: convergence rates and conditioning
Reference 42
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Miller, and Richard Peng
Reference 43
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A universal catalyst for first-order optimization
Reference 44
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Stability of the lanczos method for matrix function approximation
Reference 45
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Spec- trum approximation beyond fast matrix multiplication: Algorithms and hardness
Reference 46
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nguyˆ en
Reference 47
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nesterov
Reference 48
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Nesterov and Sebastian U
Reference 49
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Algorithm Design Using Spectral Graph Theory
Reference 50
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Sparsified block elimination for directed laplacians
Reference 51
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A fast randomized algorithm for overdetermined linear least-squares regression
Reference 52
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Improved approximation algorithms for large matrices via random projections
Reference 53
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Spielman and Shang-Hua Teng
Reference 54
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A Note on Preconditioning by Low-Stretch Spanning Trees
Reference 55
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Gaussian elimination is not optimal
Reference 56
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure A randomized Kaczmarz algorithm with exponential conver- gence
Reference 57
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Unresolved cited work
Reference 58
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Multiplying matrices faster than coppersmith-winograd
Reference 59
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Observation 09be5b42-666d-486d-bb0d-5f42c17416a3 · outbound
Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure New bounds for matrix multiplication: from alpha to omega
Reference 60
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Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Sketching as a tool for numerical linear algebra
Reference 61
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Observation c48e1435-0088-47b4-870f-ce2c8a51d85d · outbound
Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure Even faster accelerated coordinate descent using non-uniform sampling
Reference 62
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No inbound Pith citation observations are available.