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Paper Citation Record · LEDGER

A trust-region framework for optimization using Hermite kernel surrogate models

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2507.01729.

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

pith.paper-citation-record.v1
2507.01729 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:52:33.189731Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:24:25.917895Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

31 of 31 outbound references displayed

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  • verified fuzzy24
  • unresolved6
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External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 77488d7e-4890-49aa-810f-35d0aab38aa8 · outbound

This paper cites Kamat, and Layne T.

A trust-region framework for optimization using Hermite kernel surrogate models Kamat, and Layne T

Reference 1

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation d634e57d-71db-4551-9461-8b494903aa5e · outbound

This paper cites an unresolved cited work.

A trust-region framework for optimization using Hermite kernel surrogate models Unresolved cited work

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 747b228f-0eb2-4c33-ac85-77e486414d4e · outbound

This paper cites Electron Correlation in Molecules Using Direct Second Order MCSCF, pages 179–206.

A trust-region framework for optimization using Hermite kernel surrogate models Electron Correlation in Molecules Using Direct Second Order MCSCF, pages 179–206

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b7a98f29-bfcd-4235-a9d4-6b5c9a8534cc · outbound

This paper cites Maximum loss for risk measurement of portfolios.

A trust-region framework for optimization using Hermite kernel surrogate models Maximum loss for risk measurement of portfolios

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 8c39320d-cad6-4a59-8068-bd92b329bc03 · outbound

This paper cites an unresolved cited work.

A trust-region framework for optimization using Hermite kernel surrogate models Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5e871ec2-8ec0-4592-820d-10f5f4df15d0 · outbound

This paper cites Alexandrov, J.

A trust-region framework for optimization using Hermite kernel surrogate models Alexandrov, J

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation bf55412e-86a0-4493-81ac-95d1ddc135d6 · outbound

This paper cites Conn, Nicholas I.

A trust-region framework for optimization using Hermite kernel surrogate models Conn, Nicholas I

Reference 7

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ffe4836f-0f5f-49a9-9c2e-707f28200a34 · outbound

This paper cites A non-conforming dual approach for adaptive trust-region reduced basis approximation of PDE-constrained parameter optimization.

A trust-region framework for optimization using Hermite kernel surrogate models A non-conforming dual approach for adaptive trust-region reduced basis approximation of PDE-constrained parameter optimization

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 92c455cd-5a60-4c09-807b-c0e2484a816e · outbound

This paper cites A certified trust region reduced basis approach to PDE-constrained optimization.SIAM J.

A trust-region framework for optimization using Hermite kernel surrogate models A certified trust region reduced basis approach to PDE-constrained optimization.SIAM J

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 4ad17921-9b24-4807-b903-c15fa180b9ac · outbound

This paper cites an unresolved cited work.

A trust-region framework for optimization using Hermite kernel surrogate models Unresolved cited work

Reference 10

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5b63f46d-24bf-4052-8717-f5d970119cb4 · outbound

This paper cites Accelerating optimization of parametric linear systems by model order reduction.

A trust-region framework for optimization using Hermite kernel surrogate models Accelerating optimization of parametric linear systems by model order reduction

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation da42831c-248f-4c3b-9c24-fb57ee37a99a · outbound

This paper cites Adaptive reduced basis trust region methods for parameter identification problems.Comput.

A trust-region framework for optimization using Hermite kernel surrogate models Adaptive reduced basis trust region methods for parameter identification problems.Comput

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0ed03032-4eb6-45b5-9314-1173a18b3f4b · outbound

This paper cites Scattered Data Approximation.

A trust-region framework for optimization using Hermite kernel surrogate models Scattered Data Approximation

Reference 13

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 570bf9ef-3e24-4478-9b6d-2e0661dcfe57 · outbound

This paper cites Carlberg, Antony Jameson, Mykel J.

A trust-region framework for optimization using Hermite kernel surrogate models Carlberg, Antony Jameson, Mykel J

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e9c6da26-dda3-4b3e-8844-acef2cb85065 · outbound

This paper cites Gaussian Process Regression for minimum energy path optimization and transition state search.J.

A trust-region framework for optimization using Hermite kernel surrogate models Gaussian Process Regression for minimum energy path optimization and transition state search.J

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b1d3c63b-b447-4597-98a5-d2d234cf4e95 · outbound

This paper cites Goal- Oriented Two-Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations.

A trust-region framework for optimization using Hermite kernel surrogate models Goal- Oriented Two-Layered Kernel Models as Automated Surrogates for Surface Kinetics in Reactor Simulations

Reference 16

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b1071e5f-d3c8-4237-9069-5e44af0f364c · outbound

This paper cites Smola.Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond.

A trust-region framework for optimization using Hermite kernel surrogate models Smola.Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 2ef666a1-7636-4fca-9fd1-195f65eb6e24 · outbound

This paper cites Springer, New York, NY, 2008.

A trust-region framework for optimization using Hermite kernel surrogate models Springer, New York, NY, 2008

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation d6499eb3-1154-4b67-a8f4-e4887fcc6dcd · outbound

This paper cites Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems.Adv.

A trust-region framework for optimization using Hermite kernel surrogate models Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems.Adv

Reference 19

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 35640ebf-9bb1-4cad-bebd-99e1d7a57617 · outbound

This paper cites Fasshauer and Qi Ye.

A trust-region framework for optimization using Hermite kernel surrogate models Fasshauer and Qi Ye

Reference 20

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e74d86ce-ea77-4bd0-85f9-7236adacb134 · outbound

This paper cites an unresolved cited work.

A trust-region framework for optimization using Hermite kernel surrogate models Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 3628b430-e1c7-43f7-bd6d-80c43550a011 · outbound

This paper cites an unresolved cited work.

A trust-region framework for optimization using Hermite kernel surrogate models Unresolved cited work

Reference 22

Resolution
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raw_fallback, observed 2026-08-06T20:52:34.388167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 4f8579aa-2f9c-42b5-9bd7-4935b453fed2 · outbound

This paper cites A new approach to variable metric algorithms.Comput.

A trust-region framework for optimization using Hermite kernel surrogate models A new approach to variable metric algorithms.Comput

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:52:34.245086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 321f4875-0d36-4ff1-921b-3aae51f5fb1a · outbound

This paper cites A family of variable-metric methods derived by variational means.Math.

A trust-region framework for optimization using Hermite kernel surrogate models A family of variable-metric methods derived by variational means.Math

Reference 24

Resolution
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raw_fallback, observed 2026-08-06T20:52:34.078900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation fb5cab0d-8b3b-47ac-954c-8448948962c6 · outbound

This paper cites an unresolved cited work.

A trust-region framework for optimization using Hermite kernel surrogate models Unresolved cited work

Reference 25

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raw_fallback, observed 2026-08-06T20:52:33.938965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e82ad365-08dd-4893-9196-5664c82b6323 · outbound

This paper cites Byrd, Mary E.

A trust-region framework for optimization using Hermite kernel surrogate models Byrd, Mary E

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:52:33.799837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 323b21bf-1571-400a-b9b3-1ec04e69e0e3 · outbound

This paper cites A software package for sequential quadratic programming.

A trust-region framework for optimization using Hermite kernel surrogate models A software package for sequential quadratic programming

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:52:33.696631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7e08a7dd-8eec-4ec7-8910-29edae88ca9a · outbound

This paper cites Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu.

A trust-region framework for optimization using Hermite kernel surrogate models Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:52:33.588160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:52:33.022896Z digest=sha256:396c380ff88e93af7f72979598a191571691ca3ad7ec435e828a619b285393b0

Observation 1bea7ef7-bb54-4499-8250-2404fc76786c · outbound

This paper cites Byrd, Peihuang Lu, and Jorge Nocedal.

A trust-region framework for optimization using Hermite kernel surrogate models Byrd, Peihuang Lu, and Jorge Nocedal

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:52:33.467587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:52:33.075255Z digest=sha256:34531a04c49ba39f6837506a34a2789f5124bee4e03e99d8df17fbd86cddb26c

Observation d457974d-6b69-4e9a-9299-2d8f1458399d · outbound

This paper cites pyMOR – Generic algorithms and interfaces for Model Order Reduction.SIAM J.

A trust-region framework for optimization using Hermite kernel surrogate models pyMOR – Generic algorithms and interfaces for Model Order Reduction.SIAM J

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:52:33.396551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T20:52:33.153270Z digest=sha256:f66aee1e67b7d6a386705ce96a3b99f0c7b2191b31cec90ff87a2b2d50091048

Observation aee63d57-03b4-4003-a361-c28f35dac19a · outbound

This paper cites A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.

A trust-region framework for optimization using Hermite kernel surrogate models A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs

Reference 31

Resolution
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raw_fallback, observed 2026-08-06T20:52:33.311066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Pith citing papers

Observation 5a260de1-0c1f-41e1-b1b4-2008195a6f53 · inbound

Adaptive Reduced Basis Trust Region Methods for Parabolic Inverse Problems cites this paper.

Adaptive Reduced Basis Trust Region Methods for Parabolic Inverse Problems A trust-region framework for optimization using Hermite kernel surrogate models

Reference 54

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local_arxiv, observed 2026-08-06T17:24:28.263756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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