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Paper Citation Record · LEDGER

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy

As of 15 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2509.08256.

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

pith.paper-citation-record.v1
2509.08256 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:04:57.235709Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T04:55:27.851849Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T04:58:05.087047Z

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 6cc2486e-a34c-4128-8b17-9a53a2fe1be8 · outbound

This paper cites Gheribi, Michael Kokkolaras, and S´ ebastien Le Digabel.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Gheribi, Michael Kokkolaras, and S´ ebastien Le Digabel

Reference 1

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Observation dac601a2-5d3d-46b1-b38b-426f3cd55b7a · outbound

This paper cites Quadratic Models.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Quadratic Models

Reference 2

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Observation de29ae58-4f7f-4f05-8890-9202f342d76a · outbound

This paper cites Scalable subspace methods for derivative- free nonlinear least-squares optimization.Math.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Scalable subspace methods for derivative- free nonlinear least-squares optimization.Math

Reference 3

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Observation 355dd6b0-1b9e-4094-b6fa-16a2cc9a12f0 · outbound

This paper cites Adaptive cubic regularisation methods for unconstrained optimization.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Adaptive cubic regularisation methods for unconstrained optimization

Reference 4

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Observation 4744d7c8-ca1a-495c-93d5-f2aa57307284 · outbound

This paper cites A Randomised Subspace Gauss-Newton Method for Nonlinear Least-Squares.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy A Randomised Subspace Gauss-Newton Method for Nonlinear Least-Squares

Reference 5

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Observation 1a0d238c-cc6a-4e8a-966c-e949194e1c46 · outbound

This paper cites Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions

Reference 6

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Observation ad4e5be9-fccc-478b-b3cf-a1c82d901544 · outbound

This paper cites Q-fully quadratic modeling and its applica- tion in a random subspace derivative-free method.Comput.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Q-fully quadratic modeling and its applica- tion in a random subspace derivative-free method.Comput

Reference 7

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Observation bc4d7f74-37e6-40d3-92c3-277532543a21 · outbound

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Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Unresolved cited work

Reference 8

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Observation bae87734-75bf-4083-b88a-3741b82920af · outbound

This paper cites Conn, Katya Scheinberg, and Lu ´ ıs N.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Conn, Katya Scheinberg, and Lu ´ ıs N

Reference 9

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Observation fb087e48-edd3-4742-beb8-7ef4f170d38b · outbound

This paper cites Dolan and Jorge J.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Dolan and Jorge J

Reference 10

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Observation 9b63ba96-acd8-4bf1-96ac-bda8f4755dc5 · outbound

This paper cites Larson, Matt Menickelly, and Stefan M.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Larson, Matt Menickelly, and Stefan M

Reference 11

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Observation b768a6c4-791e-469f-b82a-0084e5823f82 · outbound

This paper cites Wild, and Pengcheng Xie.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Wild, and Pengcheng Xie

Reference 12

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Observation 052cee0b-27d5-493f-b46f-b1363c220721 · outbound

This paper cites John Wiley & Sons, Ltd, USA, 2000.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy John Wiley & Sons, Ltd, USA, 2000

Reference 13

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Observation b8a1d724-e970-4484-93e5-f66017712245 · outbound

This paper cites Stochastic first- and zeroth-order methods for non- convex stochastic programming.SIAM J.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Stochastic first- and zeroth-order methods for non- convex stochastic programming.SIAM J

Reference 14

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Observation 0f626e23-6c9a-48a9-84e8-8124781d674a · outbound

This paper cites RSN: Randomized Subspace Newton.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy RSN: Randomized Subspace Newton

Reference 15

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Observation 79eec735-a9e2-4661-b72b-963f4f488162 · outbound

This paper cites Expected decrease for derivative-free algorithms using random subspaces.Math.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Expected decrease for derivative-free algorithms using random subspaces.Math

Reference 16

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Observation ee7d38a5-fe3e-4185-8aee-cd9616f940e2 · outbound

This paper cites New subspace method for unconstrained derivative-free optimization.ACM Trans.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy New subspace method for unconstrained derivative-free optimization.ACM Trans

Reference 17

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Observation 1ac483ad-f804-4741-80e7-585f45f584a0 · outbound

This paper cites Liu and Jorge Nocedal.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Liu and Jorge Nocedal

Reference 18

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Observation 8590bc50-343e-44fd-a650-f13bd7e4fb95 · outbound

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Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Unresolved cited work

Reference 19

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Observation 7e55bef9-c4a0-40b6-941a-fea085da5f76 · outbound

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Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Unresolved cited work

Reference 20

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Observation b2150d6c-adbf-459f-a382-be25995a4238 · outbound

This paper cites Scalable Second-Order Optimization Algorithms for Minimizing Low-rank Functions.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Scalable Second-Order Optimization Algorithms for Minimizing Low-rank Functions

Reference 21

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Observation 30f7007e-7597-4ca5-80cd-f236447695bd · outbound

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Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Unresolved cited work

Reference 22

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Observation f19c45f5-a14f-442b-bacb-f750f7e006ea · outbound

This paper cites Woodruff.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Woodruff

Reference 23

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Observation ccea7b31-70cb-4e5f-9f41-e2b3cbd516fc · outbound

This paper cites ReMU: Regional Minimal Updating for Model-Based Derivative-Free Optimization.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy ReMU: Regional Minimal Updating for Model-Based Derivative-Free Optimization

Reference 24

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Observation 758df8cd-d044-4ddd-a1ab-ec24bcfe238f · outbound

This paper cites Least H2 norm updating of quadratic interpolation models for derivative-free trust-region algorithms.IMA Journal of Numerical Analysis, page drae106, 03 2025.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Least H2 norm updating of quadratic interpolation models for derivative-free trust-region algorithms.IMA Journal of Numerical Analysis, page drae106, 03 2025

Reference 25

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Observation 56a5cd5f-77d0-4622-bab0-45bef493e367 · outbound

This paper cites A New Two-dimensional Model-based Subspace Method for Large-scale Unconstrained Derivative-free Optimization: 2D-MoSub.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy A New Two-dimensional Model-based Subspace Method for Large-scale Unconstrained Derivative-free Optimization: 2D-MoSub

Reference 26

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 44958967-f415-4633-8301-37361bb7e10a · outbound

This paper cites Derivative-free optimization with transformed ob- jective functions (DFOTO) and the algorithm based on the least Frobenius norm up- dating quadratic model.J.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Derivative-free optimization with transformed ob- jective functions (DFOTO) and the algorithm based on the least Frobenius norm up- dating quadratic model.J

Reference 27

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Observation c686db85-755e-4f63-96fb-b953ad0834fc · outbound

This paper cites A derivative-free method using a new under- determined quadratic interpolation model.SIAM J.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy A derivative-free method using a new under- determined quadratic interpolation model.SIAM J

Reference 28

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Observation 7cfb9c79-ed45-4470-8458-7a1de144a5b5 · outbound

This paper cites Robinson, and Rene Vidal.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Robinson, and Rene Vidal

Reference 29

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Observation 7b8f92e3-0c3b-4d16-ba30-fcba1f288160 · outbound

This paper cites On the truncated conjugate gradient method.Math.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy On the truncated conjugate gradient method.Math

Reference 30

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Observation 8830ac2b-28d8-437f-b82d-5d980b1d724d · outbound

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Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Unresolved cited work

Reference 31

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Observation f7e923e5-eb43-42d7-9d43-f74b0839c9ab · outbound

This paper cites DRSOM: A Dimension Reduced Second-Order Method.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy DRSOM: A Dimension Reduced Second-Order Method

Reference 32

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Observation 47b63c24-0954-4931-8f97-fe6073b40254 · outbound

This paper cites Scalable Derivative-Free Optimization Algorithms with Low-Dimensional Subspace Techniques.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy Scalable Derivative-Free Optimization Algorithms with Low-Dimensional Subspace Techniques

Reference 33

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Observation 89b1eee1-31e2-4054-98ac-e861a7a49a99 · outbound

This paper cites URLhttp://epubs.siam.org/doi/book/10.1137/ 1.9780898718768.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy URLhttp://epubs.siam.org/doi/book/10.1137/ 1.9780898718768

Reference 2009

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Observation 3b64e6a6-3beb-4d58-b66e-377b487bc672 · outbound

This paper cites doi:10.1145/3618297.

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy doi:10.1145/3618297

Reference 2023

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source=pdf_text observed=2026-08-04T21:04:57.194008Z digest=sha256:f9eb62406aad93aec0a8472e35c440ac758908b7e63dcfc5df311b5d28ecf1b5

Pith citing papers

Observation 0da372c3-27f6-46d8-8972-34f7c985171f · inbound

Distributed Gradient-Regularized Newton Method: Scheduled Consensus and O(epsilon^{-1}) Global Iteration Complexity cites this paper.

Distributed Gradient-Regularized Newton Method: Scheduled Consensus and O(epsilon^{-1}) Global Iteration Complexity Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy

Reference 43

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arxiv_id, observed 2026-05-20T04:58:05.090559Z

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source=pdf_text observed=2026-05-20T04:55:27.851849Z digest=sha256:ad892999fe9866eb579fe965f6b34b63aa1d8739ece4956839b9f8dabe7bb09e